Copy_of_human_rl_v2.ipynb

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      "source": [
        "# Using RL to Model Cognitive Tasks\n",
        "\n",
        "**By Neurmatch Academy**\n",
        "\n",
        "__Content creators:__ Morteza Ansarinia, Yamil Vidal\n",
        "\n",
        "__Production editor:__ Spiros Chavlis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "84B_IMDlHA-T"
      },
      "source": [
        "<p align='center'><img src='https://github.com/NeuromatchAcademy/widgets/blob/master/sponsors.png?raw=True'/></p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "DUcgD2CGHA-U"
      },
      "source": [
        "---\n",
        "# Objective\n",
        "\n",
        "- This project aims to use behavioral data to train an agent and then use the agent to investigate data produced by human subjects. Having a computational agent that mimics humans in such tests, we will be able to compare its mechanics with human data.\n",
        "\n",
        "- In another conception, we could fit an agent that learns many cognitive tasks that require abstract-level constructs such as executive functions. This is a multi-task control problem.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "dqlJguOgHA-W"
      },
      "source": [
        "---\n",
        "# Setup"
      ]
    },
    {
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      ],
      "source": [
        "# @title Install dependencies\n",
        "# !pip install --upgrade pip setuptools wheel --quiet\n",
        "!pip install dm-acme[jax,tensorflow] --quiet\n",
        "!pip install dm-acme[envs] --quiet\n",
        "!pip install dm-sonnet --quiet\n",
        "!pip install trfl --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {},
        "id": "lYr0Rp3oHA-e"
      },
      "outputs": [],
      "source": [
        "# Imports\n",
        "\n",
        "import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sonnet as snt\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import dm_env\n",
        "\n",
        "import acme\n",
        "from acme import specs\n",
        "from acme import wrappers\n",
        "from acme import EnvironmentLoop\n",
        "from acme.agents.tf import dqn\n",
        "from acme.utils import loggers\n",
        "from tqdm import tqdm "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "execution": {},
        "id": "h6qra9eCHA-e"
      },
      "outputs": [],
      "source": [
        "# @title Figure settings\n",
        "from IPython.display import clear_output, display, HTML\n",
        "%matplotlib inline\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "amLHDeiXHA-f"
      },
      "source": [
        "---\n",
        "# Background\n",
        "\n",
        "- Cognitive scientists use standard lab tests to tap into specific processes in the brain and behavior. Some examples of those tests are Stroop, N-back, Digit Span, TMT (Trail making tests), and WCST (Wisconsin Card Sorting Tests).\n",
        "\n",
        "- Despite an extensive body of research that explains human performance using descriptive what-models, we still need a more sophisticated approach to gain a better understanding of the underlying processes (i.e., a how-model).\n",
        "\n",
        "- Interestingly, many of such tests can be thought of as a continuous stream of stimuli and corresponding actions, that is in consonant with the RL formulation. In fact, RL itself is in part motivated by how the brain enables goal-directed behaviors using reward systems, making it a good choice to explain human performance.\n",
        "\n",
        "- One behavioral test example would be the N-back task.\n",
        "\n",
        "  - In the N-back, participants view a sequence of stimuli, one by one, and are asked to categorize each stimulus as being either match or non-match. Stimuli are usually numbers, and feedback is given at both timestep and trajectory levels.\n",
        "\n",
        "  - The agent is rewarded when its response matches the stimulus that was shown N steps back in the episode. A simpler version of the N-back uses two-choice action schema, that is match vs non-match. Once the present stimulus matches the one presented N step back, then the agent is expected to respond to it as being a `match`.\n",
        "\n",
        "\n",
        "- Given a trained RL agent, we then find correlates of its fitted parameters with the brain mechanisms. The most straightforward composition could be the correlation of model parameters with the brain activities."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "jSi9vuPjHA-g"
      },
      "source": [
        "## Datasets\n",
        "\n",
        "- HCP WM task ([NMA-CN HCP notebooks](https://github.com/NeuromatchAcademy/course-content/tree/master/projects/fMRI))\n",
        "\n",
        "Any dataset that used cognitive tests would work.\n",
        "Question: limit to behavioral data vs fMRI?\n",
        "Question: Which stimuli and actions to use?\n",
        "classic tests can be modeled using 1) bounded symbolic stimuli/actions (e.g., A, B, C), but more sophisticated one would require texts or images (e.g., face vs neutral images in social stroop dataset)\n",
        "The HCP dataset from NMA-CN contains behavioral and imaging data for 7 cognitive tests including various versions of N-back."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "IKeEqNEZHA-h"
      },
      "source": [
        "## N-back task\n",
        "\n",
        "In the N-back task, participants view a sequence of stimuli, one per time, and are asked to categorize each stimulus as being either match or non-match. Stimuli are usually numbers, and feedbacks are given at both timestep and trajectory levels.\n",
        "\n",
        "In a typical neuro setup, both accuracy and response time are measured, but here, for the sake of brevity, we focus only on accuracy of responses."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "CANdLa1CHA-i"
      },
      "source": [
        "---\n",
        "# Cognitive Tests Environment\n",
        "\n",
        "First we develop an environment in that agents perform a cognitive test, here the N-back."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "ZJW-_yYdHA-j"
      },
      "source": [
        "## Human dataset\n",
        "\n",
        "We need a dataset of human perfoming a N-back test, with the following features:\n",
        "\n",
        "- `participant_id`: following the BIDS format, it contains a unique identifier for each participant.\n",
        "- `trial_index`: same as `time_step`.\n",
        "- `stimulus`: same as `observation`.\n",
        "- `response`: same as `action`, recorded response by the human subject.\n",
        "- `expected_response`: correct response.\n",
        "- `is_correct`: same as `reward`, whether the human subject responded correctly.\n",
        "- `response_time`: won't be used here.\n",
        "\n",
        "Here we generate a mock dataset with those features, but remember to **replace this with real human data.**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### generate mock data"
      ],
      "metadata": {
        "id": "3w1cnw25HBxT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {},
        "id": "-LAaobwDHA-j"
      },
      "outputs": [],
      "source": [
        "def generate_mock_nback_dataset(N=2,\n",
        "                                n_participants=10,\n",
        "                                n_trials=32,\n",
        "                                stimulus_choices=list('ABCDEF'),\n",
        "                                response_choices=['match', 'non-match']):\n",
        "  \"\"\"Generate a mock dataset for the N-back task.\"\"\"\n",
        "\n",
        "  n_rows = n_participants * n_trials\n",
        "  n_s = len(stimulus_choices)\n",
        "  print(n_s)\n",
        "  participant_ids = sorted([f'sub-{pid}' for pid in range(1, n_participants + 1)] * n_trials)\n",
        "  trial_indices = list(range(1, n_trials + 1)) * n_participants\n",
        "  prob = 0.5 * (n_s - 2)/ (n_s - 1)\n",
        " # prob = 0.4\n",
        "  print(prob)\n",
        "#   stimulus_sequence = np.random.choice(stimulus_choices, n_rows)\n",
        "\n",
        "  # change stimuluse_sequence to control match and non-match number\n",
        "  stimulus_sequence=np.ones(n_rows)\n",
        "  # match prob will be slightly higher than non-match prob\n",
        "  for i in range(n_rows):\n",
        "    if i<N:\n",
        "        stimulus_sequence[i]=np.random.choice(n_s)\n",
        "    else:\n",
        "        rand=np.random.random()\n",
        "        if rand < prob:\n",
        "            stimulus_sequence[i]=stimulus_sequence[i-N]\n",
        "        else:\n",
        "            stimulus_sequence[i]=np.random.choice(n_s)\n",
        "\n",
        "\n",
        "  responses = np.random.choice(response_choices, n_rows)\n",
        "  response_times = np.random.exponential(size=n_rows)\n",
        "\n",
        "  df = pd.DataFrame({\n",
        "      'participant_id': participant_ids,\n",
        "      'trial_index': trial_indices,\n",
        "      'stimulus': stimulus_sequence,\n",
        "      'response': responses,\n",
        "      'response_time': response_times\n",
        "  })\n",
        "\n",
        "  # mark matchig stimuli\n",
        "  _nback_stim = df['stimulus'].shift(N)\n",
        "  df['expected_response'] = (df['stimulus'] == _nback_stim).map({True: 'match', False: 'non-match'})\n",
        "\n",
        "  df['is_correct'] = (df['response'] == df['expected_response'])\n",
        "\n",
        "  # we don't care about burn-in trials (trial < N)\n",
        "  df.loc[df['trial_index'] <= N, 'is_correct'] = True\n",
        "  df.loc[df['trial_index'] <= N, ['response', 'response_time', 'expected_response']] = None\n",
        "\n",
        "  return df\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analyze mock data"
      ],
      "metadata": {
        "id": "9OUzQxO1G7L7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# ========\n",
        "# now generate the actual data with the provided function and plot some of its features\n",
        "N=2\n",
        "episode_steps=100\n",
        "mock_nback_data1 = generate_mock_nback_dataset()\n",
        "mock_nback_data1['is_correct'] = mock_nback_data1['is_correct'].astype(int)\n",
        "\n",
        "\n",
        "# sns.displot(data=mock_nback_data, x='response_time')\n",
        "# plt.suptitle('response time distribution of the mock N-back dataset', y=1.01)\n",
        "# plt.show()\n",
        "\n",
        "# sns.displot(data=mock_nback_data, x='is_correct')\n",
        "# plt.suptitle('Accuracy distribution of the mock N-back dataset', y=1.06)\n",
        "# plt.show()\n",
        "\n",
        "# sns.barplot(data=mock_nback_data, y='is_correct', x='participant_id')\n",
        "# plt.suptitle('Accuracy distribution of the mock N-back dataset', y=1.06)\n",
        "# plt.show()\n",
        "\n",
        "mock_nback_data1.head()\n",
        "\n",
        "sns.displot(data=mock_nback_data1, x='expected_response')\n",
        "plt.suptitle('correct-action distribution of the mock N-back dataset', y=1.06)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
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      "execution_count": null,
      "outputs": [
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          "text": [
            "6\n",
            "0.4\n"
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              "<Figure size 360x360 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "dG-N0OPuHA-k"
      },
      "source": [
        "## Implementation scheme\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "G-po6h3IHA-k"
      },
      "source": [
        "### Environment\n",
        "\n",
        "The following cell implments N-back envinronment, that we later use to train a RL agent on human data. It is capable of performing two kinds of simulation:\n",
        "- rewards the agent once the action was correct (i.e., a normative model of the environment).\n",
        "- receives human data (or mock data if you prefer), and returns what participants performed as the observation. This is more useful for preference-based RL."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {},
        "id": "0bW8nFsQHA-k"
      },
      "outputs": [],
      "source": [
        "class NBack(dm_env.Environment):\n",
        "\n",
        "  ACTIONS = ['match', 'non-match']\n",
        "\n",
        "  def __init__(self,\n",
        "               N=2,\n",
        "               episode_steps=32,\n",
        "               stimuli_choices=list('ABCDEF'),\n",
        "               human_data=None,\n",
        "               seed=1,\n",
        "               ):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "      N: Number of steps to look back for the matched stimuli. Defaults to 2 (as in 2-back).\n",
        "      episode_steps\n",
        "      stimuli_choices\n",
        "      human_data\n",
        "      seed\n",
        "\n",
        "    \"\"\"\n",
        "    self.N = N\n",
        "    self.episode_steps = episode_steps\n",
        "    self.stimuli_choices = stimuli_choices\n",
        "    self.stimuli = np.empty(shape=episode_steps)  # will be filled in the `reset()`\n",
        "\n",
        "    self._reset_next_step = True\n",
        "\n",
        "    # whether mimic humans or reward the agent once it responds optimally.\n",
        "    if human_data is None:\n",
        "      self._imitate_human = False\n",
        "      self.human_data = None\n",
        "      self.human_subject_data = None\n",
        "    else:\n",
        "      self._imitate_human = True\n",
        "      self.human_data = human_data\n",
        "      self.human_subject_data = None\n",
        "\n",
        "    self._action_history = []\n",
        "\n",
        "  def reset(self):\n",
        "    self._reset_next_step = False\n",
        "    self._current_step = 0\n",
        "    self._action_history.clear()\n",
        "\n",
        "    # generate a random sequence instead of relying on human data\n",
        "    if self.human_data is None:\n",
        "      # self.stimuli = np.random.choice(self.stimuli_choices, self.episode_steps)\n",
        "      # FIXME This is a fix for acme & reverb issue with string observation. Agent should be able to handle strings\n",
        "      self.stimuli = np.random.choice(len(self.stimuli_choices), self.episode_steps).astype(np.float32)\n",
        "    #   self.stimuli = np.empty(episode_steps)\n",
        "    #   # match prob will be slightly higher than non-match prob\n",
        "    #   for i in range(episode_steps):\n",
        "    #     if i<N:\n",
        "    #         self.stimuli[i]=np.random.choice(len(self.stimuli_choices))\n",
        "    #     else:\n",
        "    #         rand=np.random.random()\n",
        "    #         if rand < 0.4:\n",
        "    #             self.stimuli[i]=self.stimuli[i-N]\n",
        "    #         else:\n",
        "    #             self.stimuli[i]=np.random.choice(len(self.stimuli_choices))\n",
        "    #   print(self.stimuli)\n",
        "    else:\n",
        "      # randomly choose a subject from the human data and follow her trials and responses.\n",
        "      # FIXME should we always use one specific human subject or randomly select one in each episode?\n",
        "      self.human_subject_data = self.human_data.query('participant_id == participant_id.sample().iloc[0]',\n",
        "                                                engine='python').sort_values('trial_index')\n",
        "      self.stimuli = self.human_subject_data['stimulus'].to_list()\n",
        "      self.stimuli = np.array([ord(s) - ord('A') + 1 for s in self.stimuli]).astype(np.float32)\n",
        "\n",
        "    return dm_env.restart(self._observation())\n",
        "\n",
        "\n",
        "  def _episode_return(self):\n",
        "    if self._imitate_human:\n",
        "      return np.mean(self.human_subject_data['response'] == self._action_history)\n",
        "    else:\n",
        "      return 0.0\n",
        "    \n",
        "\n",
        "  def step(self, action: int):\n",
        "    if self._reset_next_step:\n",
        "      return self.reset()\n",
        "\n",
        "    agent_action = NBack.ACTIONS[action]\n",
        "\n",
        "    if self._imitate_human:\n",
        "      # if it was the same action as the human subject, then reward the agent\n",
        "      human_action = self.human_subject_data['response'].iloc[self._current_step]\n",
        "      step_reward = 0. if (agent_action == human_action) else -1.\n",
        "    else:\n",
        "      # assume the agent is rationale and doesn't want to reproduce human, reward once the response it correct\n",
        "      expected_action = 'match' if (self.stimuli[self._current_step] == self.stimuli[self._current_step - self.N]) else 'non-match'\n",
        "      step_reward = 0. if (agent_action == expected_action) else -1.\n",
        "\n",
        "    self._action_history.append(agent_action)\n",
        "\n",
        "    self._current_step += 1\n",
        "\n",
        "    # Check for termination.\n",
        "    if self._current_step == self.stimuli.shape[0]:\n",
        "      self._reset_next_step = True\n",
        "      # we are using the mean of total time step rewards as the episode return\n",
        "      return dm_env.termination(reward=self._episode_return(),\n",
        "                                observation=self._observation())\n",
        "    else:\n",
        "      return dm_env.transition(reward=step_reward,\n",
        "                               observation=self._observation())\n",
        "\n",
        "  def observation_spec(self):\n",
        "    return dm_env.specs.BoundedArray(\n",
        "        shape=self.stimuli.shape,\n",
        "        dtype=self.stimuli.dtype,\n",
        "        name='nback_stimuli', minimum=0, maximum=len(self.stimuli_choices) + 1)\n",
        "\n",
        "  def action_spec(self):\n",
        "    return dm_env.specs.DiscreteArray(\n",
        "        num_values=len(NBack.ACTIONS),\n",
        "        dtype=np.int32,\n",
        "        name='action')\n",
        "\n",
        "  def _observation(self):\n",
        "\n",
        "    # agent observes only the current trial\n",
        "    # obs = self.stimuli[self._current_step - 1]\n",
        "\n",
        "    # agents observe stimuli up to the current trial\n",
        "    obs = self.stimuli[:self._current_step+1].copy()\n",
        "    obs = np.pad(obs,(0, len(self.stimuli) - len(obs)))\n",
        "\n",
        "    return obs\n",
        "\n",
        "  def plot_state(self):\n",
        "    \"\"\"Display current state of the environment.\n",
        "\n",
        "     Note: `M` mean `match`, and `.` is a `non-match`.\n",
        "    \"\"\"\n",
        "    stimuli = self.stimuli[:self._current_step - 1]\n",
        "    actions = ['M' if a=='match' else '.' for a in self._action_history[:self._current_step - 1]]\n",
        "    actions_binary = [1 if a=='M' else 0 for a in actions] \n",
        "    expected_actions = stimuli[:-self.N]==stimuli[self.N:]\n",
        "    rewards = [1 if a==e else 0   for a in actions for e in expected_actions]\n",
        "    return HTML(\n",
        "        f'<b>Environment ({self.N}-back):</b><br />'\n",
        "        f'<pre><b>Stimuli:</b> {\"\".join(map(str,map(int,stimuli)))}</pre>'\n",
        "        f'<pre><b>Actions:</b> {\"\".join(actions)}</pre>'\n",
        "        \n",
        "\n",
        "    )\n",
        "    # f'<pre><b>Rewards:</b> {\"\".join(map(str,reward)}</pre>'\n",
        "    # print(self.N,'back')\n",
        "    # print('stimuli: ',stimuli)\n",
        "    # print('actions: ',actions)\n",
        "    # print('answers: ',expected_actions)\n",
        "    # print('rewards: ',rewards)\n",
        "\n",
        "\n",
        "  @staticmethod\n",
        "  def create_environment(N,episode_steps):\n",
        "    \"\"\"Utility function to create a N-back environment and its spec.\"\"\"\n",
        "\n",
        "    # Make sure the environment outputs single-precision floats.\n",
        "    environment = wrappers.SinglePrecisionWrapper(NBack(N,episode_steps))\n",
        "\n",
        "    # Grab the spec of the environment.\n",
        "    environment_spec = specs.make_environment_spec(environment)\n",
        "\n",
        "    return environment, environment_spec"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "pFm0KrNPHA-l"
      },
      "source": [
        "### Define a random agent\n",
        "\n",
        "For more information you can refer to NMA-DL W3D2 Basic Reinforcement learning."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {},
        "id": "FmQl3jeeHA-l"
      },
      "outputs": [],
      "source": [
        "class RandomAgent(acme.Actor):\n",
        "\n",
        "  def __init__(self, environment_spec):\n",
        "    \"\"\"Gets the number of available actions from the environment spec.\"\"\"\n",
        "    self._num_actions = environment_spec.actions.num_values\n",
        "\n",
        "  def select_action(self, observation):\n",
        "    \"\"\"Selects an action uniformly at random.\"\"\"\n",
        "    action = np.random.randint(self._num_actions)\n",
        "    return action\n",
        "\n",
        "  def observe_first(self, timestep):\n",
        "    \"\"\"Does not record as the RandomAgent has no use for data.\"\"\"\n",
        "    pass\n",
        "\n",
        "  def observe(self, action, next_timestep):\n",
        "    \"\"\"Does not record as the RandomAgent has no use for data.\"\"\"\n",
        "    pass\n",
        "\n",
        "  def update(self):\n",
        "    \"\"\"Does not update as the RandomAgent does not learn from data.\"\"\"\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define DQN"
      ],
      "metadata": {
        "id": "u5GW44FzN1LB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def dqn_make_network(action_spec: specs.DiscreteArray) -> snt.Module:\n",
        "  return snt.Sequential([\n",
        "      snt.Flatten(),\n",
        "      snt.nets.MLP([50, 50, 50, 50, action_spec.num_values]),\n",
        "  ])\n"
      ],
      "metadata": {
        "id": "CksSLmV8uUZc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define DRQN"
      ],
      "metadata": {
        "id": "kd2YnP4yO3vZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# add recurrent layer to dqn\n",
        "# r2d2 receive sequence data\n",
        "from acme.agents.tf import r2d2\n",
        "def r2d2_make_network(action_spec: specs.DiscreteArray) -> snt.Module:\n",
        "  return snt.DeepRNN([\n",
        "            snt.Flatten(),\n",
        "            snt.LSTM(2),\n",
        "            snt.nets.MLP([50, 50, action_spec.num_values])\n",
        "            ])\n"
      ],
      "metadata": {
        "id": "GucpcNWauYrx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot function"
      ],
      "metadata": {
        "id": "wBSMenJ5hpVF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot moving average\n",
        "def moving_average(interval, window_size):\n",
        "    window = np.ones(int(window_size)) / float(window_size)\n",
        "    return np.convolve(interval, window, 'same')\n",
        "\n",
        "# plot reward by episode\n",
        "def plot_reward_by_episode(logs_dict):\n",
        "    for key, val in logs_dict.items():\n",
        "        logs=logs_dict[key]\n",
        "        reward_ma=moving_average(logs['episode_return'],10)\n",
        "        plt.plot(reward_ma)\n",
        "\n",
        "    plt.ylabel(\"episode reward\")\n",
        "    plt.xlabel(\"episode\")\n",
        "    plt.legend(['RANDOM','DQN','DRQN'])\n",
        "    plt.title(\"total reward within episode\")\n"
      ],
      "metadata": {
        "id": "Zjy8fcedvZSd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test plot function \n",
        "# N = 2\n",
        "# n_episodes = 1000\n",
        "# episode_steps= 32\n",
        "plot_reward_by_episode(logs_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "ari38aBF26C0",
        "outputId": "2b5056c0-5412-47fb-8a60-a094718c1984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "xz3x4BLhHA-n"
      },
      "source": [
        "## Test agents"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Parameters"
      ],
      "metadata": {
        "id": "2STbPQfOd2Ml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fitting parameters\n",
        "N=2\n",
        "n_episodes = 1000\n",
        "episode_steps= 100\n",
        "n_total_steps = 0\n",
        "log_loss = False\n",
        "n_steps = n_episodes * episode_steps\n"
      ],
      "metadata": {
        "id": "nOrWWtRMdz0J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Environment"
      ],
      "metadata": {
        "id": "JrbKkZmdd5EG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {},
        "id": "OLnRt42yHA-r",
        "outputId": "f56d06d2-0cbe-410e-c9d1-c5f68cb8d81e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "actions:\n",
            " DiscreteArray(shape=(), dtype=int32, name=action, minimum=0, maximum=1, num_values=2)\n",
            "observations:\n",
            " BoundedArray(shape=(32,), dtype=dtype('float32'), name='nback_stimuli', minimum=0.0, maximum=7.0)\n",
            "rewards:\n",
            " Array(shape=(), dtype=dtype('float32'), name='reward')\n"
          ]
        }
      ],
      "source": [
        "env, env_spec = NBack.create_environment(N=2,episode_steps=32)\n",
        "agent = RandomAgent(env_spec)\n",
        "\n",
        "print('actions:\\n', env_spec.actions)\n",
        "print('observations:\\n', env_spec.observations)\n",
        "print('rewards:\\n', env_spec.rewards)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### test Random Agent"
      ],
      "metadata": {
        "id": "doart4swZ5yX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {},
        "id": "qI6T1LwqHA-v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "350f0e96-f938-4364-d2b8-523d54b15289"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-126-a8b84f9d43f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m   \u001b[0;31m# Make the first observation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m   \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobserve_first\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m   \u001b[0;31m# Run an episode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/acme/agents/agent.py\u001b[0m in \u001b[0;36mobserve_first\u001b[0;34m(self, timestep)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mobserve_first\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimestep\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdm_env\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeStep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_actor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobserve_first\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mobserve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNestedArray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext_timestep\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdm_env\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeStep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/acme/agents/tf/actors.py\u001b[0m in \u001b[0;36mobserve_first\u001b[0;34m(self, timestep)\u001b[0m\n\u001b[1;32m    164\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mobserve_first\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimestep\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdm_env\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeStep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_adder\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_adder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_first\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m     \u001b[0;31m# Set the state to None so that we re-initialize at the next policy call.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/acme/adders/reverb/base.py\u001b[0m in \u001b[0;36madd_first\u001b[0;34m(self, timestep)\u001b[0m\n\u001b[1;32m    174\u001b[0m     self._writer.append(dict(observation=timestep.observation,\n\u001b[1;32m    175\u001b[0m                              start_of_episode=timestep.first()),\n\u001b[0;32m--> 176\u001b[0;31m                         partial_step=True)\n\u001b[0m\u001b[1;32m    177\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_first_called\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/reverb/trajectory_writer.py\u001b[0m in \u001b[0;36mappend\u001b[0;34m(self, data, partial_step)\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0;31m# Flatten the data and pass it to the C++ writer for column wise append.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpartial_step\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m       \u001b[0mflat_column_data_references\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAppendPartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat_column_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m       \u001b[0mflat_column_data_references\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAppend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat_column_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Tensor of incompatible shape provided for column 0. Got [0] which is incompatible with [100]."
          ]
        }
      ],
      "source": [
        "# all_returns = []\n",
        "# # main loop\n",
        "# for episode in range(n_episodes):\n",
        "#   episode_steps = 0\n",
        "#   episode_return = 0\n",
        "#   episode_loss = 0\n",
        "\n",
        "#   start_time = time.time()\n",
        "\n",
        "#   timestep = env.reset()\n",
        "\n",
        "#   # Make the first observation.\n",
        "#   agent.observe_first(timestep)\n",
        "\n",
        "#   # Run an episode\n",
        "#   while not timestep.last():\n",
        "\n",
        "#     # DEBUG\n",
        "#     # print(timestep)\n",
        "\n",
        "#     # Generate an action from the agent's policy and step the environment.\n",
        "#     action = agent.select_action(timestep.observation)\n",
        "#     timestep = env.step(action)\n",
        "\n",
        "#     # Have the agent observe the timestep and let the agent update itself.\n",
        "#     agent.observe(action, next_timestep=timestep)\n",
        "#     agent.update()\n",
        "\n",
        "#     # Book-keeping.\n",
        "#     episode_steps += 1\n",
        "#     n_total_steps += 1\n",
        "#     episode_return += timestep.reward\n",
        "\n",
        "#     if log_loss:\n",
        "#       episode_loss += agent.last_loss\n",
        "\n",
        "#     if n_steps is not None and n_total_steps >= n_steps:\n",
        "#       break\n",
        "\n",
        "#   # Collect the results and combine with counts.\n",
        "#   steps_per_second = episode_steps / (time.time() - start_time)\n",
        "#   result = {\n",
        "#       'episode': episode,\n",
        "#       'episode_length': episode_steps,\n",
        "#       'episode_return': episode_return,\n",
        "#   }\n",
        "#   if log_loss:\n",
        "#     result['loss_avg'] = episode_loss/episode_steps\n",
        "\n",
        "#   all_returns.append(episode_return)\n",
        "\n",
        "#   display(env.plot_state())\n",
        "#   # Log the given results.\n",
        "#   print(result)\n",
        "\n",
        "#   if n_steps is not None and n_total_steps >= n_steps:\n",
        "#     break\n",
        "\n",
        "# clear_output()\n",
        "\n",
        "# # Histogram of all returns\n",
        "# plt.figure()\n",
        "# sns.histplot(all_returns, stat=\"density\", kde=True, bins=12)\n",
        "# plt.xlabel('Return [a.u.]')\n",
        "# plt.ylabel('Density')\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "ZwxHabM5HA-x"
      },
      "source": [
        "**Note:** You can simplify the environment loop using [DeepMind Acme](https://github.com/deepmind/acme)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### test DQN"
      ],
      "metadata": {
        "id": "-OF2iOiUOL35"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {},
        "id": "y_S1FJvRHA-y"
      },
      "outputs": [],
      "source": [
        "# # test dqn\n",
        "# # construct a DQN agent\n",
        "agent = dqn.DQN(\n",
        "    environment_spec=env_spec,\n",
        "    network=dqn_make_network(env_spec.actions),\n",
        "    epsilon=[0.1],\n",
        "    logger=loggers.InMemoryLogger(),\n",
        "    checkpoint=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "execution": {},
        "id": "9TTVcl5fHA-z"
      },
      "source": [
        "Now, we run the environment loop with the DQN agent and print the training log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {},
        "id": "6a5rvXerHA-z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "07d8b5e6-8edd-444c-ecca-80f53054eeb4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     episode_length episode_return  steps_per_second  episodes  steps\n",
              "995              32           -5.0        321.527336       996  31872\n",
              "996              32           -6.0        340.099097       997  31904\n",
              "997              32           -4.0        359.265739       998  31936\n",
              "998              32           -4.0        359.887082       999  31968\n",
              "999              32           -5.0        360.187444      1000  32000"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>episode_length</th>\n",
              "      <th>episode_return</th>\n",
              "      <th>steps_per_second</th>\n",
              "      <th>episodes</th>\n",
              "      <th>steps</th>\n",
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              "      <th>995</th>\n",
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              "      <td>321.527336</td>\n",
              "      <td>996</td>\n",
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              "    <tr>\n",
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              "      <td>340.099097</td>\n",
              "      <td>997</td>\n",
              "      <td>31904</td>\n",
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              "      <th>997</th>\n",
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              "      <td>359.265739</td>\n",
              "      <td>998</td>\n",
              "      <td>31936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
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              "      <td>359.887082</td>\n",
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              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>32</td>\n",
              "      <td>-5.0</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-21166779-61fd-4375-b777-add642819525 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-21166779-61fd-4375-b777-add642819525');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "# # training loop\n",
        "n_episodes = 1\n",
        "loop = EnvironmentLoop(env, agent, logger=loggers.InMemoryLogger())\n",
        "loop.run(n_episodes)\n",
        "\n",
        "# print logs\n",
        "logs_dqn = pd.DataFrame(loop._logger._data)\n",
        "logs_dqn.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### test DRQN"
      ],
      "metadata": {
        "id": "lwP03QS4INDO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # test DRQN\n",
        "# # construct a DRQN agent\n",
        "# agent_r2d2 = r2d2.R2D2(\n",
        "#     environment_spec=env_spec,\n",
        "#     network=r2d2_make_network(env_spec.actions),\n",
        "#     burn_in_length=10,\n",
        "#     trace_length=2,\n",
        "#     replay_period=10,\n",
        "#     epsilon=[0.1],\n",
        "#     logger=loggers.InMemoryLogger(),\n",
        "#     checkpoint=False,\n",
        "# )\n",
        "\n",
        "# loop = EnvironmentLoop(env, agent_r2d2, logger=loggers.InMemoryLogger())\n",
        "# loop.run(n_episodes)\n",
        "\n",
        "# # print logs\n",
        "# logs_r2d2 = pd.DataFrame(loop._logger._data)\n",
        "# logs_r2d2.tail()"
      ],
      "metadata": {
        "id": "1YJ2JM_PHZvx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Playground"
      ],
      "metadata": {
        "id": "l1hhXaIIfvzQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## test different parameters"
      ],
      "metadata": {
        "id": "UAVvoTrU-jRU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# test 5 back\n",
        "\n",
        "N = 2\n",
        "n_episodes = 100\n",
        "episode_steps= 32\n",
        "n_total_steps = 0\n",
        "log_loss = False\n",
        "n_steps = n_episodes * episode_steps\n",
        "# mock data\n",
        "# mock_nback_data = generate_mock_nback_dataset(N=N,n_trials=episode_steps)\n",
        "# environment\n",
        "env, env_spec = NBack.create_environment(N=N,episode_steps=episode_steps)\n",
        "# agent\n",
        "agent_random = RandomAgent(env_spec)\n",
        "\n",
        "agent_dqn = dqn.DQN(\n",
        "        environment_spec=env_spec,\n",
        "        network=dqn_make_network(env_spec.actions),\n",
        "        epsilon=[0.1],\n",
        "        logger=loggers.InMemoryLogger(),\n",
        "        checkpoint=False,\n",
        ")\n",
        "\n",
        "agent_drqn = r2d2.R2D2(\n",
        "    environment_spec=env_spec,\n",
        "    network=r2d2_make_network(env_spec.actions),\n",
        "    burn_in_length=10,\n",
        "    trace_length=2,\n",
        "    replay_period=10,\n",
        "    epsilon=[0.1],\n",
        "    logger=loggers.InMemoryLogger(),\n",
        "    checkpoint=False,\n",
        ")\n",
        "\n",
        "# training loop\n",
        "agent_dict= {'RANDOM':agent_random,\n",
        "    'DQN':agent_dqn,\n",
        "    'DRQN':agent_drqn}\n",
        "logs_dict = {}\n",
        "\n",
        "for key, agent in tqdm(agent_dict.items()):\n",
        "    loop = EnvironmentLoop(env, agent, logger=loggers.InMemoryLogger())\n",
        "    loop.run(n_episodes)\n",
        "    logs_dict[key] = pd.DataFrame(loop._logger._data)\n",
        "    # print(logs_dict[key].tail(5))\n",
        "\n"
      ],
      "metadata": {
        "id": "_9rMoNVqUPOQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ebc5dc9-e19a-456a-9c9e-2a8be88c4d88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/3 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2. 3. 2. 1. 4. 1. 4. 2. 2. 2. 2. 1. 2. 1. 0. 1. 0. 4. 0. 4. 3. 3. 1. 3.\n",
            " 0. 1. 0. 1. 4. 1. 4. 4.]\n",
            "[1. 3. 0. 5. 1. 5. 0. 5. 3. 5. 3. 4. 0. 2. 2. 1. 4. 1. 4. 3. 4. 3. 4. 3.\n",
            " 2. 1. 2. 1. 0. 3. 4. 3.]\n",
            "[0. 2. 3. 5. 3. 5. 3. 5. 0. 5. 4. 3. 4. 5. 4. 5. 4. 2. 4. 2. 4. 2. 0. 5.\n",
            " 5. 5. 4. 5. 2. 0. 2. 0.]\n",
            "[1. 4. 1. 2. 1. 2. 0. 2. 1. 2. 1. 4. 1. 4. 1. 0. 2. 3. 5. 3. 5. 3. 5. 5.\n",
            " 2. 4. 2. 5. 3. 2. 3. 1.]\n",
            "[1. 1. 1. 4. 1. 0. 1. 0. 5. 0. 0. 0. 0. 0. 4. 0. 4. 2. 2. 2. 1. 0. 1. 1.\n",
            " 1. 4. 1. 4. 3. 4. 3. 4.]\n",
            "[3. 4. 4. 0. 4. 3. 4. 1. 3. 5. 3. 3. 1. 3. 5. 0. 5. 0. 5. 5. 5. 5. 0. 1.\n",
            " 5. 5. 5. 5. 5. 0. 1. 0.]\n",
            "[2. 2. 0. 3. 0. 3. 0. 0. 1. 5. 1. 3. 0. 3. 0. 3. 5. 0. 0. 0. 0. 4. 0. 0.\n",
            " 4. 2. 2. 4. 4. 4. 3. 4.]\n",
            "[4. 2. 0. 4. 0. 4. 0. 1. 3. 1. 2. 5. 3. 1. 3. 1. 4. 1. 4. 1. 2. 4. 2. 5.\n",
            " 4. 5. 0. 5. 1. 5. 0. 2.]\n",
            "[2. 5. 2. 5. 2. 5. 2. 5. 2. 3. 3. 3. 3. 4. 3. 4. 1. 4. 0. 5. 4. 1. 4. 1.\n",
            " 5. 0. 5. 2. 1. 2. 1. 3.]\n",
            "[0. 4. 0. 4. 0. 2. 0. 2. 0. 3. 0. 3. 3. 3. 3. 3. 1. 3. 0. 2. 0. 2. 0. 0.\n",
            " 1. 0. 1. 1. 0. 1. 4. 4.]\n",
            "[0. 5. 5. 3. 5. 1. 3. 5. 3. 5. 1. 1. 1. 3. 3. 2. 4. 2. 4. 1. 4. 0. 4. 0.\n",
            " 4. 3. 0. 2. 0. 2. 0. 2.]\n",
            "[2. 2. 2. 2. 3. 1. 3. 3. 3. 2. 3. 5. 3. 5. 5. 5. 2. 2. 2. 2. 2. 2. 1. 2.\n",
            " 4. 1. 4. 1. 3. 1. 4. 1.]\n",
            "[2. 3. 3. 0. 0. 5. 3. 1. 3. 1. 4. 3. 0. 1. 0. 1. 5. 1. 5. 2. 0. 2. 0. 4.\n",
            " 0. 4. 0. 4. 0. 4. 1. 1.]\n",
            "[3. 5. 5. 1. 3. 3. 5. 4. 4. 4. 4. 1. 0. 1. 5. 1. 5. 2. 5. 2. 5. 0. 5. 0.\n",
            " 5. 4. 1. 4. 1. 4. 4. 0.]\n",
            "[4. 1. 4. 0. 4. 0. 5. 0. 5. 2. 0. 2. 2. 0. 2. 3. 2. 3. 2. 3. 5. 3. 5. 4.\n",
            " 2. 3. 1. 3. 1. 0. 3. 1.]\n",
            "[4. 2. 4. 0. 4. 4. 4. 1. 4. 1. 0. 1. 4. 1. 4. 1. 4. 1. 0. 1. 2. 1. 1. 0.\n",
            " 5. 0. 5. 4. 5. 1. 0. 1.]\n",
            "[2. 4. 5. 3. 1. 3. 4. 3. 4. 3. 3. 3. 5. 3. 3. 3. 3. 0. 0. 0. 0. 0. 0. 5.\n",
            " 3. 3. 4. 3. 2. 0. 2. 0.]\n",
            "[5. 3. 5. 4. 3. 4. 3. 4. 3. 4. 5. 4. 1. 4. 1. 4. 1. 0. 5. 4. 2. 1. 4. 2.\n",
            " 1. 1. 3. 1. 0. 1. 0. 1.]\n",
            "[0. 3. 0. 3. 0. 3. 3. 5. 0. 5. 2. 5. 5. 5. 5. 5. 5. 0. 5. 4. 5. 5. 5. 4.\n",
            " 1. 4. 2. 4. 2. 3. 4. 3.]\n",
            "[5. 5. 5. 5. 5. 3. 2. 5. 2. 5. 0. 3. 0. 3. 2. 3. 5. 4. 3. 1. 5. 4. 5. 4.\n",
            " 4. 4. 5. 4. 5. 3. 5. 4.]\n",
            "[1. 2. 1. 4. 2. 0. 5. 4. 4. 4. 5. 4. 5. 4. 3. 4. 4. 1. 1. 4. 5. 5. 5. 5.\n",
            " 0. 2. 0. 3. 3. 2. 2. 2.]\n",
            "[0. 5. 0. 5. 0. 5. 5. 2. 5. 2. 5. 5. 4. 5. 4. 0. 4. 0. 4. 1. 4. 1. 4. 0.\n",
            " 4. 0. 4. 4. 5. 3. 5. 3.]\n",
            "[1. 1. 1. 1. 4. 4. 4. 2. 3. 0. 1. 0. 1. 0. 4. 0. 0. 1. 1. 1. 1. 1. 0. 3.\n",
            " 4. 2. 0. 0. 0. 0. 0. 0.]\n",
            "[5. 3. 5. 3. 2. 3. 2. 3. 1. 4. 4. 4. 3. 4. 1. 4. 4. 3. 4. 3. 0. 1. 0. 5.\n",
            " 0. 5. 0. 2. 0. 0. 4. 4.]\n",
            "[0. 1. 0. 5. 0. 5. 1. 1. 5. 2. 3. 2. 1. 2. 2. 3. 5. 3. 2. 3. 5. 3. 5. 3.\n",
            " 1. 4. 1. 5. 3. 5. 5. 5.]\n",
            "[2. 5. 2. 5. 2. 5. 2. 3. 2. 0. 4. 4. 5. 0. 0. 5. 5. 0. 3. 3. 3. 5. 3. 5.\n",
            " 3. 2. 5. 0. 2. 0. 2. 0.]\n",
            "[2. 3. 2. 5. 0. 1. 0. 1. 3. 3. 4. 3. 0. 2. 2. 2. 2. 2. 2. 3. 4. 2. 5. 2.\n",
            " 5. 2. 5. 2. 2. 5. 5. 1.]\n",
            "[2. 0. 2. 0. 2. 0. 0. 0. 0. 0. 3. 0. 5. 0. 5. 3. 5. 3. 5. 3. 5. 3. 5. 4.\n",
            " 5. 3. 5. 3. 5. 5. 5. 5.]\n",
            "[0. 0. 0. 3. 0. 2. 0. 0. 0. 3. 1. 0. 0. 5. 0. 4. 1. 4. 4. 4. 4. 3. 4. 4.\n",
            " 4. 1. 3. 1. 3. 4. 3. 2.]\n",
            "[2. 3. 2. 0. 2. 3. 0. 4. 0. 4. 1. 4. 3. 0. 3. 2. 3. 2. 1. 2. 1. 2. 1. 5.\n",
            " 1. 2. 1. 2. 5. 3. 5. 4.]\n",
            "[2. 5. 3. 4. 0. 4. 2. 4. 5. 4. 5. 0. 0. 1. 0. 5. 0. 0. 0. 4. 2. 2. 4. 2.\n",
            " 1. 2. 3. 0. 5. 0. 4. 0.]\n",
            "[4. 0. 4. 4. 2. 5. 2. 5. 2. 3. 1. 3. 1. 4. 1. 1. 4. 4. 3. 4. 1. 4. 1. 4.\n",
            " 1. 4. 1. 5. 4. 3. 4. 1.]\n",
            "[0. 5. 0. 5. 4. 5. 2. 2. 3. 2. 4. 3. 4. 2. 3. 1. 3. 3. 0. 3. 0. 2. 0. 2.\n",
            " 2. 2. 2. 2. 1. 4. 1. 0.]\n",
            "[4. 4. 4. 3. 4. 3. 4. 3. 4. 4. 2. 4. 0. 4. 1. 0. 1. 3. 0. 3. 0. 3. 0. 3.\n",
            " 0. 3. 0. 3. 2. 3. 4. 3.]\n",
            "[3. 2. 3. 5. 3. 5. 3. 2. 3. 2. 4. 4. 4. 4. 4. 3. 3. 0. 3. 5. 1. 5. 4. 4.\n",
            " 4. 5. 4. 5. 4. 5. 4. 5.]\n",
            "[1. 5. 3. 3. 0. 0. 0. 0. 0. 0. 0. 2. 0. 1. 1. 1. 1. 2. 3. 4. 3. 4. 5. 4.\n",
            " 5. 3. 5. 4. 5. 2. 5. 2.]\n",
            "[4. 1. 4. 0. 4. 3. 5. 2. 5. 2. 5. 2. 2. 3. 5. 2. 0. 2. 1. 3. 4. 3. 4. 3.\n",
            " 4. 3. 2. 4. 0. 3. 4. 0.]\n",
            "[3. 3. 3. 0. 3. 0. 3. 2. 3. 5. 4. 5. 1. 0. 1. 2. 0. 2. 0. 2. 5. 3. 4. 0.\n",
            " 4. 4. 4. 4. 4. 2. 2. 2.]\n",
            "[4. 0. 4. 0. 0. 1. 0. 1. 0. 5. 0. 5. 3. 3. 3. 3. 2. 5. 2. 3. 2. 0. 2. 4.\n",
            " 2. 3. 2. 0. 3. 2. 5. 2.]\n",
            "[4. 5. 4. 5. 4. 2. 1. 2. 1. 2. 0. 1. 2. 0. 3. 0. 5. 0. 1. 3. 1. 3. 1. 3.\n",
            " 4. 5. 0. 5. 1. 5. 1. 2.]\n",
            "[4. 0. 2. 0. 2. 0. 2. 0. 3. 0. 4. 4. 0. 4. 0. 4. 0. 2. 0. 1. 4. 1. 4. 1.\n",
            " 2. 2. 4. 0. 0. 5. 0. 5.]\n",
            "[4. 4. 4. 4. 4. 5. 0. 5. 0. 5. 0. 5. 1. 5. 4. 5. 5. 5. 4. 1. 5. 1. 0. 4.\n",
            " 0. 4. 3. 4. 1. 4. 2. 4.]\n",
            "[2. 1. 1. 3. 3. 2. 3. 1. 3. 5. 5. 5. 5. 5. 5. 5. 5. 1. 5. 1. 5. 1. 2. 2.\n",
            " 5. 4. 1. 4. 0. 4. 0. 4.]\n",
            "[0. 0. 3. 0. 3. 0. 3. 2. 3. 0. 1. 5. 4. 5. 0. 5. 0. 3. 0. 4. 0. 4. 0. 0.\n",
            " 1. 5. 5. 5. 5. 5. 0. 5.]\n",
            "[4. 2. 4. 3. 4. 3. 4. 3. 4. 3. 4. 3. 5. 0. 0. 1. 0. 2. 3. 2. 2. 2. 2. 5.\n",
            " 2. 4. 0. 4. 0. 4. 4. 1.]\n",
            "[5. 4. 5. 4. 5. 0. 0. 4. 5. 4. 2. 5. 3. 3. 0. 0. 2. 3. 2. 3. 2. 3. 2. 1.\n",
            " 1. 5. 1. 2. 1. 2. 4. 3.]\n",
            "[2. 3. 2. 1. 2. 1. 2. 3. 2. 1. 2. 4. 3. 4. 0. 4. 0. 4. 0. 3. 0. 1. 0. 2.\n",
            " 1. 1. 5. 3. 2. 3. 2. 5.]\n",
            "[3. 1. 5. 0. 2. 4. 0. 4. 3. 4. 5. 4. 1. 0. 3. 0. 2. 5. 2. 0. 2. 3. 2. 3.\n",
            " 2. 3. 2. 3. 4. 3. 2. 0.]\n",
            "[2. 5. 5. 3. 5. 3. 5. 3. 5. 3. 5. 2. 2. 2. 2. 4. 5. 3. 5. 3. 2. 3. 2. 1.\n",
            " 2. 0. 1. 2. 5. 5. 3. 5.]\n",
            "[0. 2. 0. 5. 5. 5. 2. 4. 2. 4. 1. 4. 5. 4. 5. 2. 5. 2. 5. 2. 0. 0. 1. 0.\n",
            " 5. 0. 3. 3. 4. 3. 4. 0.]\n",
            "[2. 0. 2. 1. 2. 1. 2. 0. 2. 1. 4. 1. 4. 4. 4. 5. 4. 0. 0. 2. 0. 2. 4. 2.\n",
            " 2. 2. 2. 3. 5. 3. 5. 2.]\n",
            "[0. 5. 1. 5. 2. 5. 0. 1. 0. 0. 5. 5. 0. 5. 0. 5. 1. 2. 1. 2. 5. 0. 3. 1.\n",
            " 3. 1. 2. 1. 2. 0. 2. 0.]\n",
            "[2. 5. 2. 3. 0. 3. 0. 4. 0. 2. 3. 2. 3. 2. 0. 2. 0. 0. 0. 0. 0. 0. 0. 3.\n",
            " 0. 4. 0. 4. 0. 5. 1. 3.]\n",
            "[0. 4. 0. 2. 4. 2. 3. 4. 3. 5. 4. 5. 4. 1. 0. 5. 5. 0. 5. 2. 4. 2. 4. 2.\n",
            " 5. 2. 4. 2. 0. 3. 0. 1.]\n",
            "[2. 1. 2. 3. 2. 3. 3. 4. 1. 5. 1. 2. 2. 3. 5. 1. 5. 1. 5. 4. 5. 4. 3. 5.\n",
            " 3. 5. 3. 3. 2. 3. 4. 0.]\n",
            "[4. 1. 4. 1. 0. 1. 0. 1. 2. 3. 5. 3. 5. 5. 3. 2. 1. 2. 1. 5. 1. 5. 0. 3.\n",
            " 1. 1. 5. 5. 3. 5. 3. 5.]\n",
            "[5. 4. 5. 5. 5. 0. 1. 0. 5. 0. 2. 0. 2. 1. 5. 0. 5. 5. 3. 5. 3. 5. 3. 5.\n",
            " 2. 5. 5. 5. 4. 4. 4. 4.]\n",
            "[2. 0. 1. 4. 1. 4. 2. 4. 5. 0. 1. 0. 4. 2. 4. 2. 4. 5. 4. 2. 5. 2. 5. 2.\n",
            " 5. 4. 5. 4. 5. 3. 3. 3.]\n",
            "[3. 4. 5. 4. 5. 4. 5. 0. 2. 0. 2. 0. 1. 3. 1. 2. 0. 4. 0. 4. 5. 1. 5. 4.\n",
            " 3. 1. 2. 1. 2. 1. 5. 2.]\n",
            "[1. 5. 4. 1. 5. 4. 5. 1. 5. 0. 0. 0. 1. 0. 0. 0. 1. 0. 5. 0. 5. 2. 4. 5.\n",
            " 0. 0. 0. 0. 4. 0. 4. 1.]\n",
            "[2. 1. 0. 1. 2. 5. 2. 5. 0. 4. 0. 3. 2. 0. 1. 0. 1. 1. 1. 3. 2. 3. 5. 4.\n",
            " 5. 0. 5. 3. 5. 3. 2. 3.]\n",
            "[5. 5. 5. 3. 1. 3. 0. 1. 0. 2. 0. 0. 5. 0. 5. 4. 5. 4. 5. 4. 5. 4. 3. 5.\n",
            " 5. 5. 5. 5. 5. 5. 4. 5.]\n",
            "[3. 4. 3. 5. 3. 5. 3. 3. 2. 3. 4. 4. 1. 4. 0. 1. 0. 5. 2. 1. 3. 1. 3. 2.\n",
            " 0. 3. 3. 4. 3. 1. 1. 1.]\n",
            "[1. 4. 3. 1. 0. 2. 3. 2. 5. 4. 2. 4. 3. 0. 3. 2. 3. 3. 0. 3. 4. 1. 2. 2.\n",
            " 2. 5. 0. 5. 0. 5. 5. 5.]\n",
            "[1. 3. 1. 3. 0. 3. 0. 5. 0. 2. 4. 3. 5. 3. 5. 3. 5. 0. 5. 1. 5. 2. 4. 2.\n",
            " 3. 2. 3. 2. 3. 2. 4. 0.]\n",
            "[0. 5. 5. 1. 0. 1. 0. 5. 0. 5. 0. 5. 1. 5. 1. 5. 4. 0. 4. 2. 2. 2. 2. 2.\n",
            " 2. 2. 4. 2. 2. 1. 2. 1.]\n",
            "[5. 5. 5. 3. 5. 3. 5. 3. 4. 3. 5. 1. 5. 3. 0. 4. 4. 5. 4. 0. 4. 3. 4. 2.\n",
            " 1. 2. 5. 1. 3. 3. 5. 1.]\n",
            "[2. 2. 2. 2. 2. 2. 3. 2. 3. 2. 3. 2. 4. 2. 4. 3. 4. 3. 4. 4. 3. 5. 3. 5.\n",
            " 3. 5. 3. 0. 3. 2. 3. 2.]\n",
            "[0. 0. 0. 0. 3. 5. 3. 5. 3. 1. 4. 3. 4. 3. 4. 0. 4. 0. 4. 0. 0. 1. 5. 4.\n",
            " 5. 4. 2. 0. 2. 2. 2. 2.]\n",
            "[0. 3. 0. 0. 0. 4. 3. 4. 4. 1. 4. 0. 4. 1. 3. 5. 3. 5. 4. 0. 4. 0. 0. 3.\n",
            " 3. 1. 0. 2. 4. 4. 4. 4.]\n",
            "[4. 0. 4. 1. 4. 5. 4. 5. 4. 0. 4. 1. 2. 2. 3. 3. 3. 3. 3. 3. 3. 1. 3. 3.\n",
            " 0. 3. 1. 1. 1. 1. 1. 1.]\n",
            "[0. 0. 0. 1. 1. 1. 1. 1. 1. 0. 5. 0. 3. 0. 3. 4. 3. 4. 3. 4. 3. 0. 2. 2.\n",
            " 1. 3. 0. 3. 0. 4. 1. 1.]\n",
            "[4. 3. 4. 2. 4. 2. 0. 5. 1. 3. 1. 3. 1. 2. 2. 5. 4. 3. 2. 3. 4. 4. 4. 1.\n",
            " 0. 1. 0. 3. 0. 3. 0. 2.]\n",
            "[1. 1. 1. 3. 1. 4. 3. 5. 3. 4. 3. 4. 1. 2. 4. 2. 0. 1. 0. 2. 0. 2. 2. 5.\n",
            " 2. 5. 2. 5. 2. 5. 5. 0.]\n",
            "[1. 3. 1. 3. 2. 2. 3. 2. 3. 1. 5. 1. 5. 1. 5. 1. 2. 1. 2. 4. 5. 4. 0. 1.\n",
            " 0. 1. 0. 0. 0. 2. 0. 2.]\n",
            "[3. 3. 3. 2. 3. 3. 3. 3. 5. 3. 0. 1. 0. 4. 1. 5. 1. 3. 4. 3. 3. 3. 2. 5.\n",
            " 2. 3. 1. 5. 1. 5. 5. 5.]\n",
            "[0. 1. 0. 1. 5. 0. 0. 4. 0. 4. 0. 2. 0. 2. 3. 2. 5. 2. 4. 1. 4. 2. 1. 2.\n",
            " 5. 2. 5. 2. 5. 3. 3. 3.]\n",
            "[5. 5. 5. 1. 4. 5. 3. 4. 3. 3. 1. 3. 0. 5. 5. 5. 0. 2. 0. 2. 0. 2. 0. 5.\n",
            " 0. 5. 0. 5. 1. 5. 5. 5.]\n",
            "[2. 2. 2. 2. 2. 5. 3. 5. 3. 5. 4. 5. 4. 5. 4. 0. 3. 0. 1. 0. 1. 5. 3. 4.\n",
            " 1. 1. 1. 4. 1. 5. 3. 4.]\n",
            "[3. 4. 3. 4. 0. 3. 0. 4. 0. 1. 0. 1. 0. 0. 0. 0. 3. 4. 4. 4. 4. 4. 4. 3.\n",
            " 3. 4. 3. 0. 3. 0. 3. 1.]\n",
            "[1. 0. 1. 0. 5. 0. 5. 0. 5. 0. 5. 0. 0. 0. 0. 0. 0. 0. 0. 4. 2. 1. 2. 1.\n",
            " 4. 2. 4. 3. 4. 3. 1. 3.]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 33%|███▎      | 1/3 [00:00<00:01,  1.77it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3. 0. 0. 1. 2. 1. 0. 3. 4. 5. 0. 5. 3. 4. 5. 4. 3. 4. 3. 1. 3. 5. 1. 5.\n",
            " 2. 3. 4. 3. 2. 3. 2. 2.]\n",
            "[0. 0. 4. 0. 4. 3. 5. 3. 2. 1. 4. 1. 5. 1. 5. 3. 5. 0. 1. 5. 0. 2. 0. 4.\n",
            " 1. 0. 1. 3. 1. 3. 1. 3.]\n",
            "[4. 1. 5. 3. 2. 3. 1. 4. 1. 4. 4. 5. 4. 0. 4. 3. 4. 5. 5. 1. 1. 3. 1. 3.\n",
            " 4. 0. 4. 0. 4. 0. 3. 0.]\n",
            "[2. 4. 2. 5. 2. 5. 2. 5. 2. 5. 2. 5. 2. 5. 2. 2. 2. 2. 2. 0. 2. 0. 5. 0.\n",
            " 1. 0. 0. 4. 1. 4. 0. 2.]\n",
            "[5. 0. 0. 1. 2. 2. 2. 3. 1. 3. 1. 3. 0. 0. 0. 0. 0. 0. 0. 1. 0. 2. 2. 2.\n",
            " 1. 5. 1. 2. 1. 4. 4. 4.]\n",
            "[4. 5. 4. 5. 3. 2. 1. 2. 4. 0. 4. 2. 4. 4. 4. 4. 5. 4. 2. 4. 4. 4. 5. 4.\n",
            " 3. 2. 3. 2. 3. 2. 3. 2.]\n",
            "[2. 1. 2. 4. 2. 4. 2. 4. 2. 2. 2. 2. 5. 2. 5. 2. 0. 2. 2. 1. 0. 2. 0. 2.\n",
            " 2. 2. 2. 4. 5. 4. 2. 4.]\n",
            "[3. 1. 3. 3. 3. 1. 4. 1. 4. 1. 3. 5. 2. 5. 5. 2. 5. 2. 4. 2. 4. 2. 3. 5.\n",
            " 1. 5. 4. 5. 4. 1. 3. 3.]\n",
            "[3. 5. 2. 4. 3. 5. 3. 0. 4. 0. 4. 1. 4. 1. 4. 1. 4. 1. 0. 3. 5. 5. 1. 5.\n",
            " 1. 1. 2. 1. 2. 1. 2. 5.]\n",
            "[0. 1. 0. 1. 1. 1. 4. 5. 3. 5. 3. 5. 1. 5. 5. 5. 0. 0. 5. 0. 3. 0. 3. 0.\n",
            " 4. 1. 4. 1. 4. 2. 4. 2.]\n",
            "[5. 3. 5. 1. 5. 1. 2. 2. 4. 4. 4. 0. 5. 0. 1. 3. 1. 3. 1. 0. 3. 0. 4. 0.\n",
            " 4. 0. 0. 1. 0. 1. 0. 1.]\n",
            "[4. 2. 5. 2. 5. 1. 5. 0. 0. 0. 3. 0. 3. 2. 0. 3. 4. 5. 3. 1. 2. 1. 2. 1.\n",
            " 2. 1. 5. 2. 0. 2. 1. 5.]\n",
            "[4. 1. 4. 1. 4. 4. 4. 4. 4. 4. 1. 4. 1. 1. 5. 0. 5. 0. 3. 0. 3. 0. 3. 1.\n",
            " 3. 0. 0. 0. 2. 0. 2. 3.]\n",
            "[1. 5. 1. 3. 1. 4. 0. 4. 0. 0. 0. 0. 0. 0. 0. 3. 0. 2. 2. 0. 2. 0. 0. 0.\n",
            " 1. 0. 3. 4. 3. 4. 3. 3.]\n",
            "[5. 5. 5. 4. 0. 3. 1. 3. 0. 1. 1. 1. 0. 1. 0. 4. 4. 4. 4. 0. 2. 0. 2. 1.\n",
            " 2. 1. 1. 3. 0. 0. 0. 5.]\n",
            "[0. 5. 0. 5. 1. 3. 3. 5. 3. 5. 5. 5. 2. 5. 2. 0. 5. 0. 2. 5. 2. 0. 2. 2.\n",
            " 5. 5. 5. 1. 3. 1. 4. 1.]\n",
            "[4. 0. 5. 0. 5. 0. 5. 0. 5. 0. 5. 0. 1. 0. 0. 2. 1. 2. 1. 2. 0. 1. 3. 1.\n",
            " 0. 4. 0. 4. 2. 1. 5. 2.]\n",
            "[3. 3. 3. 1. 3. 1. 3. 1. 4. 3. 3. 3. 0. 4. 3. 0. 3. 2. 5. 4. 5. 4. 2. 0.\n",
            " 3. 2. 5. 2. 5. 0. 5. 4.]\n",
            "[0. 0. 0. 1. 0. 5. 0. 1. 2. 0. 0. 0. 0. 0. 0. 1. 0. 4. 0. 0. 1. 3. 1. 4.\n",
            " 0. 2. 0. 2. 0. 2. 0. 2.]\n",
            "[5. 3. 2. 2. 2. 2. 2. 1. 2. 0. 2. 5. 1. 0. 2. 0. 1. 2. 2. 2. 0. 0. 4. 1.\n",
            " 4. 3. 0. 5. 0. 0. 0. 0.]\n",
            "[0. 4. 3. 1. 3. 0. 3. 3. 0. 3. 3. 3. 0. 2. 3. 0. 3. 0. 5. 4. 0. 4. 0. 4.\n",
            " 0. 4. 2. 5. 2. 5. 0. 1.]\n",
            "[4. 3. 4. 4. 4. 5. 4. 0. 1. 4. 1. 3. 5. 4. 5. 4. 5. 5. 5. 0. 3. 3. 3. 3.\n",
            " 3. 3. 0. 3. 1. 2. 3. 2.]\n",
            "[0. 2. 0. 1. 5. 0. 5. 2. 5. 1. 5. 1. 5. 3. 3. 5. 3. 5. 3. 2. 1. 0. 4. 0.\n",
            " 4. 0. 3. 4. 4. 5. 3. 5.]\n",
            "[3. 5. 3. 5. 4. 4. 4. 4. 4. 4. 4. 0. 4. 3. 4. 3. 4. 3. 2. 3. 2. 2. 0. 3.\n",
            " 0. 3. 2. 3. 4. 0. 4. 3.]\n",
            "[4. 2. 1. 2. 5. 4. 1. 4. 1. 4. 3. 4. 3. 3. 5. 3. 5. 3. 5. 4. 5. 4. 5. 2.\n",
            " 5. 4. 0. 0. 0. 5. 3. 5.]\n",
            "[5. 4. 5. 5. 5. 5. 5. 3. 5. 4. 5. 4. 5. 0. 4. 0. 2. 0. 3. 3. 3. 0. 3. 5.\n",
            " 2. 4. 1. 4. 1. 4. 1. 1.]\n",
            "[1. 2. 1. 2. 1. 4. 1. 4. 4. 4. 4. 4. 2. 0. 5. 0. 5. 3. 5. 3. 1. 3. 1. 5.\n",
            " 1. 5. 1. 5. 1. 4. 5. 3.]\n",
            "[1. 1. 5. 0. 1. 0. 5. 1. 2. 0. 2. 0. 3. 0. 3. 2. 0. 2. 0. 2. 1. 2. 2. 2.\n",
            " 2. 2. 2. 2. 2. 2. 0. 0.]\n",
            "[2. 1. 4. 1. 0. 1. 1. 1. 3. 4. 0. 4. 0. 2. 4. 3. 2. 2. 2. 0. 2. 4. 0. 4.\n",
            " 0. 4. 3. 4. 3. 4. 3. 0.]\n",
            "[1. 0. 4. 0. 4. 5. 4. 3. 3. 3. 1. 3. 1. 0. 1. 0. 3. 2. 4. 5. 4. 4. 4. 4.\n",
            " 3. 4. 3. 2. 3. 0. 2. 1.]\n",
            "[3. 5. 3. 5. 3. 4. 5. 4. 5. 2. 0. 2. 0. 1. 3. 1. 4. 1. 3. 1. 1. 1. 3. 3.\n",
            " 1. 5. 2. 5. 4. 0. 4. 1.]\n",
            "[0. 2. 0. 2. 2. 2. 2. 2. 2. 0. 4. 5. 4. 5. 5. 5. 4. 3. 4. 3. 3. 1. 5. 1.\n",
            " 5. 1. 5. 2. 0. 2. 0. 2.]\n",
            "[2. 5. 2. 5. 4. 5. 3. 5. 2. 0. 1. 3. 5. 3. 0. 1. 4. 0. 4. 0. 0. 4. 1. 5.\n",
            " 1. 1. 1. 5. 1. 5. 4. 1.]\n",
            "[2. 1. 4. 1. 4. 0. 5. 3. 5. 1. 3. 1. 3. 5. 4. 1. 1. 0. 1. 2. 2. 2. 0. 2.\n",
            " 0. 0. 4. 0. 3. 2. 4. 5.]\n",
            "[5. 5. 5. 5. 1. 5. 3. 5. 3. 3. 3. 3. 0. 0. 4. 0. 4. 0. 4. 3. 4. 3. 4. 0.\n",
            " 0. 4. 0. 4. 5. 4. 5. 4.]\n",
            "[2. 2. 2. 0. 2. 4. 0. 4. 1. 4. 1. 4. 1. 1. 1. 4. 1. 4. 1. 1. 1. 1. 1. 0.\n",
            " 1. 2. 2. 5. 2. 0. 2. 3.]\n",
            "[2. 3. 2. 3. 5. 2. 1. 4. 2. 4. 2. 4. 2. 3. 4. 1. 4. 1. 0. 4. 0. 5. 0. 5.\n",
            " 1. 5. 1. 2. 1. 5. 1. 1.]\n",
            "[5. 1. 0. 0. 4. 1. 4. 1. 2. 3. 3. 5. 3. 5. 1. 5. 1. 2. 2. 2. 2. 2. 1. 2.\n",
            " 1. 1. 1. 5. 4. 5. 4. 5.]\n",
            "[1. 5. 1. 5. 1. 5. 1. 3. 5. 2. 5. 4. 0. 4. 3. 3. 3. 3. 3. 4. 3. 1. 0. 1.\n",
            " 2. 1. 2. 1. 2. 1. 2. 1.]\n",
            "[0. 3. 1. 2. 3. 2. 5. 1. 5. 1. 4. 0. 4. 0. 4. 5. 4. 2. 4. 5. 2. 5. 2. 5.\n",
            " 0. 5. 0. 4. 0. 4. 0. 5.]\n",
            "[4. 1. 2. 1. 1. 4. 1. 3. 4. 5. 4. 0. 4. 0. 3. 5. 1. 5. 1. 5. 2. 0. 2. 0.\n",
            " 2. 4. 3. 0. 3. 0. 3. 0.]\n",
            "[3. 1. 0. 0. 0. 3. 0. 3. 0. 3. 0. 1. 3. 3. 1. 0. 5. 0. 5. 0. 5. 0. 5. 0.\n",
            " 1. 0. 3. 1. 5. 1. 5. 1.]\n",
            "[4. 2. 0. 2. 0. 4. 0. 4. 0. 4. 0. 5. 2. 5. 1. 5. 1. 1. 0. 3. 2. 3. 2. 3.\n",
            " 1. 3. 1. 3. 0. 3. 0. 3.]\n",
            "[4. 0. 5. 0. 5. 0. 5. 3. 0. 0. 0. 0. 0. 3. 3. 3. 2. 1. 0. 1. 0. 3. 0. 3.\n",
            " 0. 3. 0. 3. 3. 0. 3. 0.]\n",
            "[2. 3. 5. 0. 5. 3. 3. 4. 1. 4. 1. 1. 2. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0.\n",
            " 1. 4. 1. 4. 1. 4. 4. 4.]\n",
            "[3. 5. 3. 5. 3. 5. 3. 1. 3. 1. 1. 1. 1. 0. 1. 2. 1. 0. 1. 4. 0. 4. 0. 4.\n",
            " 1. 4. 4. 5. 4. 5. 4. 4.]\n",
            "[0. 5. 5. 4. 5. 4. 1. 4. 3. 4. 3. 0. 2. 5. 5. 2. 4. 2. 4. 2. 1. 0. 1. 0.\n",
            " 1. 3. 0. 3. 0. 4. 0. 1.]\n",
            "[4. 5. 4. 4. 4. 2. 1. 1. 5. 1. 5. 5. 5. 0. 2. 0. 2. 0. 0. 0. 2. 1. 2. 2.\n",
            " 2. 2. 2. 5. 1. 4. 2. 3.]\n",
            "[2. 5. 5. 4. 5. 4. 0. 4. 0. 4. 0. 4. 0. 1. 4. 0. 4. 3. 4. 3. 4. 0. 2. 5.\n",
            " 2. 0. 2. 0. 2. 3. 2. 3.]\n",
            "[1. 1. 3. 3. 5. 3. 5. 2. 2. 2. 2. 4. 5. 4. 5. 2. 5. 5. 5. 5. 5. 0. 4. 0.\n",
            " 4. 3. 4. 3. 4. 1. 2. 1.]\n",
            "[3. 0. 3. 0. 3. 0. 5. 3. 0. 3. 5. 0. 3. 0. 1. 2. 1. 2. 4. 5. 0. 5. 4. 5.\n",
            " 4. 5. 3. 2. 3. 0. 5. 1.]\n",
            "[4. 3. 1. 4. 1. 4. 1. 4. 1. 0. 1. 0. 1. 0. 1. 0. 3. 2. 2. 3. 5. 3. 5. 3.\n",
            " 2. 1. 5. 3. 5. 3. 4. 1.]\n",
            "[5. 4. 4. 0. 4. 0. 5. 0. 2. 0. 0. 3. 0. 3. 0. 3. 0. 1. 0. 0. 1. 1. 1. 0.\n",
            " 1. 0. 1. 5. 5. 5. 4. 5.]\n",
            "[3. 0. 3. 0. 0. 0. 0. 4. 5. 4. 2. 0. 2. 0. 2. 1. 2. 3. 2. 2. 4. 1. 2. 3.\n",
            " 2. 0. 2. 1. 2. 1. 2. 1.]\n",
            "[3. 3. 1. 3. 0. 5. 5. 5. 2. 3. 3. 3. 3. 3. 3. 3. 0. 0. 1. 0. 1. 0. 3. 5.\n",
            " 3. 5. 3. 5. 5. 5. 0. 3.]\n",
            "[0. 4. 0. 4. 0. 3. 4. 2. 2. 2. 4. 4. 4. 3. 4. 0. 4. 5. 4. 5. 0. 3. 1. 0.\n",
            " 1. 3. 3. 3. 3. 0. 3. 0.]\n",
            "[1. 5. 1. 4. 4. 0. 4. 0. 1. 0. 1. 0. 1. 4. 1. 5. 2. 5. 3. 1. 1. 0. 1. 2.\n",
            " 1. 2. 1. 2. 1. 2. 1. 4.]\n",
            "[4. 2. 4. 2. 2. 1. 5. 3. 5. 3. 2. 3. 1. 0. 1. 0. 1. 2. 1. 2. 1. 2. 1. 2.\n",
            " 3. 5. 3. 5. 3. 5. 0. 5.]\n",
            "[5. 0. 5. 0. 5. 0. 4. 0. 0. 5. 1. 2. 1. 2. 5. 0. 1. 5. 1. 5. 3. 1. 3. 4.\n",
            " 0. 0. 0. 0. 0. 1. 0. 1.]\n",
            "[0. 2. 0. 2. 0. 0. 0. 4. 2. 5. 2. 5. 4. 3. 4. 3. 1. 5. 5. 1. 5. 2. 0. 0.\n",
            " 1. 0. 2. 2. 3. 0. 5. 0.]\n",
            "[3. 2. 5. 1. 5. 1. 0. 5. 3. 5. 3. 5. 3. 5. 3. 4. 3. 1. 3. 1. 1. 5. 1. 0.\n",
            " 1. 0. 2. 0. 4. 0. 4. 0.]\n",
            "[2. 1. 2. 1. 3. 1. 3. 1. 3. 5. 2. 5. 2. 3. 1. 4. 1. 4. 1. 5. 1. 5. 4. 5.\n",
            " 4. 5. 4. 5. 2. 4. 3. 0.]\n",
            "[3. 1. 1. 0. 5. 0. 5. 0. 1. 0. 5. 0. 5. 0. 0. 0. 0. 0. 2. 2. 0. 2. 0. 1.\n",
            " 3. 1. 4. 2. 3. 1. 3. 0.]\n",
            "[3. 3. 5. 3. 5. 0. 5. 0. 5. 1. 5. 3. 0. 3. 0. 2. 1. 2. 2. 1. 3. 1. 3. 1.\n",
            " 0. 5. 3. 3. 3. 3. 3. 3.]\n",
            "[0. 4. 2. 0. 2. 0. 2. 3. 5. 5. 1. 0. 1. 0. 0. 3. 5. 5. 0. 3. 0. 3. 0. 4.\n",
            " 5. 5. 5. 5. 1. 2. 3. 4.]\n",
            "[0. 5. 1. 4. 2. 4. 5. 4. 5. 5. 5. 3. 5. 3. 3. 1. 0. 5. 0. 0. 2. 1. 4. 1.\n",
            " 0. 1. 3. 1. 5. 3. 3. 5.]\n",
            "[0. 3. 0. 3. 0. 3. 5. 1. 1. 0. 1. 4. 5. 4. 1. 0. 1. 2. 3. 4. 3. 5. 0. 5.\n",
            " 4. 5. 0. 5. 5. 1. 3. 4.]\n",
            "[0. 4. 0. 4. 5. 2. 2. 2. 0. 1. 1. 5. 0. 3. 1. 3. 3. 3. 3. 4. 3. 5. 3. 3.\n",
            " 4. 5. 2. 5. 5. 0. 1. 0.]\n",
            "[3. 0. 3. 5. 1. 5. 1. 1. 4. 4. 4. 4. 4. 4. 3. 1. 3. 4. 3. 0. 5. 1. 5. 1.\n",
            " 5. 5. 1. 1. 1. 1. 1. 3.]\n",
            "[0. 3. 0. 3. 0. 3. 0. 0. 4. 0. 0. 1. 3. 4. 3. 3. 0. 4. 4. 4. 1. 4. 1. 5.\n",
            " 1. 2. 5. 2. 5. 0. 5. 0.]\n",
            "[2. 1. 2. 1. 2. 1. 2. 2. 2. 2. 2. 2. 4. 4. 3. 5. 3. 5. 3. 5. 2. 0. 2. 0.\n",
            " 2. 2. 4. 4. 5. 4. 5. 4.]\n",
            "[1. 3. 1. 5. 1. 5. 3. 5. 5. 0. 3. 0. 2. 0. 4. 0. 4. 5. 5. 2. 0. 4. 0. 4.\n",
            " 0. 4. 0. 4. 0. 5. 0. 0.]\n",
            "[4. 1. 5. 5. 0. 1. 0. 0. 5. 0. 5. 0. 4. 2. 1. 2. 1. 2. 5. 2. 0. 0. 0. 5.\n",
            " 4. 3. 4. 3. 4. 3. 2. 3.]\n",
            "[5. 1. 2. 1. 2. 1. 2. 4. 0. 4. 1. 3. 2. 2. 4. 2. 4. 3. 0. 3. 0. 3. 4. 3.\n",
            " 3. 3. 2. 3. 2. 3. 5. 3.]\n",
            "[1. 4. 2. 2. 2. 4. 2. 4. 2. 4. 4. 2. 0. 2. 5. 0. 3. 0. 2. 0. 2. 1. 2. 3.\n",
            " 5. 3. 0. 1. 4. 3. 4. 3.]\n",
            "[2. 1. 0. 1. 5. 1. 2. 3. 2. 3. 2. 3. 0. 3. 0. 5. 3. 1. 3. 1. 2. 3. 2. 3.\n",
            " 5. 3. 5. 2. 4. 4. 2. 4.]\n",
            "[0. 4. 5. 3. 5. 3. 3. 3. 3. 4. 0. 1. 0. 0. 0. 0. 0. 0. 2. 5. 1. 5. 3. 1.\n",
            " 3. 0. 3. 1. 3. 1. 3. 1.]\n",
            "[5. 2. 5. 3. 5. 2. 1. 2. 0. 5. 0. 5. 3. 1. 1. 3. 1. 0. 1. 0. 1. 0. 1. 0.\n",
            " 4. 4. 4. 4. 4. 4. 3. 1.]\n",
            "[5. 1. 5. 1. 5. 1. 0. 1. 3. 0. 0. 3. 0. 1. 0. 2. 0. 4. 0. 2. 0. 5. 4. 5.\n",
            " 1. 1. 5. 2. 0. 2. 3. 1.]\n",
            "[5. 3. 5. 2. 5. 1. 1. 2. 2. 1. 3. 3. 2. 3. 3. 3. 3. 3. 3. 2. 1. 2. 2. 2.\n",
            " 2. 2. 5. 2. 4. 2. 4. 2.]\n",
            "[5. 2. 5. 2. 5. 2. 2. 1. 0. 4. 1. 4. 1. 2. 1. 2. 5. 2. 3. 2. 3. 2. 4. 5.\n",
            " 4. 5. 4. 5. 1. 5. 1. 5.]\n",
            "[2. 0. 2. 2. 1. 2. 1. 0. 0. 0. 5. 0. 5. 0. 1. 2. 2. 1. 2. 1. 2. 1. 2. 2.\n",
            " 0. 3. 0. 3. 2. 0. 0. 0.]\n",
            "[1. 4. 3. 4. 3. 4. 3. 2. 3. 3. 2. 3. 1. 4. 5. 3. 5. 2. 3. 2. 0. 4. 4. 4.\n",
            " 3. 1. 3. 1. 0. 0. 0. 5.]\n",
            "[4. 5. 4. 1. 0. 4. 0. 4. 3. 4. 3. 5. 1. 5. 1. 5. 5. 4. 1. 4. 3. 3. 5. 4.\n",
            " 5. 0. 1. 4. 1. 4. 0. 4.]\n",
            "[3. 5. 0. 5. 2. 4. 2. 2. 0. 2. 0. 2. 1. 3. 4. 3. 2. 3. 1. 2. 3. 2. 3. 0.\n",
            " 3. 3. 3. 4. 5. 1. 5. 1.]\n",
            "[1. 4. 1. 4. 1. 4. 1. 3. 1. 3. 1. 0. 4. 0. 5. 4. 5. 0. 4. 4. 4. 4. 4. 2.\n",
            " 4. 2. 2. 2. 2. 2. 3. 2.]\n",
            "[3. 1. 0. 3. 2. 3. 4. 3. 4. 3. 4. 3. 4. 2. 4. 2. 2. 5. 2. 5. 2. 2. 0. 0.\n",
            " 3. 0. 1. 0. 1. 0. 1. 0.]\n",
            "[5. 1. 0. 5. 5. 2. 0. 1. 1. 1. 1. 2. 1. 2. 5. 2. 2. 0. 1. 5. 1. 5. 3. 4.\n",
            " 2. 0. 2. 4. 3. 4. 3. 4.]\n",
            "[5. 5. 0. 3. 0. 2. 0. 5. 3. 5. 4. 5. 5. 1. 5. 1. 5. 1. 5. 1. 3. 1. 5. 0.\n",
            " 4. 0. 4. 3. 1. 3. 5. 4.]\n",
            "[0. 5. 0. 5. 4. 5. 4. 5. 3. 5. 0. 5. 0. 5. 0. 2. 3. 2. 1. 0. 1. 0. 1. 2.\n",
            " 3. 2. 5. 0. 4. 0. 4. 0.]\n",
            "[2. 4. 0. 2. 3. 2. 3. 0. 3. 0. 3. 0. 5. 3. 5. 3. 3. 3. 5. 2. 3. 2. 0. 2.\n",
            " 0. 4. 0. 0. 4. 0. 0. 1.]\n",
            "[1. 4. 1. 4. 1. 0. 1. 0. 2. 0. 2. 5. 0. 2. 0. 2. 0. 5. 0. 3. 0. 4. 0. 0.\n",
            " 5. 0. 4. 0. 2. 0. 3. 0.]\n",
            "[2. 2. 5. 2. 5. 2. 0. 1. 0. 1. 1. 1. 1. 0. 3. 4. 0. 4. 0. 0. 5. 0. 5. 0.\n",
            " 0. 4. 4. 4. 1. 4. 1. 3.]\n",
            "[2. 1. 5. 5. 3. 2. 1. 1. 0. 3. 4. 3. 4. 3. 5. 3. 2. 3. 3. 3. 5. 2. 3. 1.\n",
            " 4. 1. 4. 3. 5. 4. 5. 4.]\n",
            "[5. 1. 5. 4. 5. 3. 2. 0. 2. 4. 3. 0. 3. 0. 1. 5. 4. 5. 4. 5. 4. 5. 4. 3.\n",
            " 4. 5. 4. 0. 5. 0. 4. 2.]\n",
            "[5. 3. 0. 2. 0. 0. 0. 4. 5. 4. 5. 4. 2. 4. 2. 4. 3. 3. 1. 3. 1. 0. 1. 0.\n",
            " 2. 5. 0. 2. 0. 1. 1. 1.]\n",
            "[2. 2. 2. 0. 2. 3. 2. 5. 4. 4. 0. 0. 0. 0. 5. 0. 5. 0. 5. 0. 5. 4. 5. 4.\n",
            " 0. 2. 1. 5. 1. 0. 1. 0.]\n",
            "[3. 3. 3. 3. 2. 3. 2. 5. 3. 5. 2. 5. 2. 4. 1. 4. 4. 4. 4. 4. 4. 4. 2. 3.\n",
            " 2. 3. 2. 4. 4. 4. 2. 4.]\n",
            "[2. 5. 3. 5. 5. 4. 3. 2. 3. 2. 5. 1. 5. 0. 2. 5. 1. 5. 1. 2. 1. 2. 3. 0.\n",
            " 3. 0. 3. 0. 3. 4. 0. 1.]\n",
            "[0. 3. 0. 1. 5. 0. 5. 4. 3. 5. 2. 2. 2. 0. 0. 0. 0. 5. 3. 1. 3. 1. 2. 0.\n",
            " 3. 2. 3. 2. 2. 5. 2. 2.]\n",
            "[4. 0. 4. 0. 3. 1. 3. 1. 4. 1. 4. 1. 3. 1. 3. 1. 3. 0. 3. 0. 2. 0. 2. 4.\n",
            " 2. 3. 2. 5. 1. 3. 4. 2.]\n",
            "[4. 3. 4. 4. 0. 3. 5. 5. 4. 3. 1. 3. 2. 2. 2. 4. 2. 4. 2. 4. 1. 4. 0. 4.\n",
            " 1. 2. 1. 0. 1. 0. 1. 4.]\n",
            "[3. 5. 2. 1. 2. 0. 2. 1. 2. 1. 2. 3. 2. 3. 0. 3. 0. 3. 3. 3. 0. 3. 2. 1.\n",
            " 3. 1. 3. 3. 3. 3. 3. 1.]\n",
            "[3. 2. 1. 0. 5. 0. 4. 0. 4. 0. 2. 0. 2. 0. 2. 4. 4. 4. 4. 2. 2. 4. 2. 4.\n",
            " 2. 4. 3. 4. 2. 3. 2. 3.]\n",
            "[2. 2. 2. 2. 1. 0. 4. 0. 4. 0. 4. 0. 3. 0. 4. 0. 4. 4. 4. 4. 4. 1. 4. 1.\n",
            " 4. 5. 4. 5. 4. 5. 4. 0.]\n",
            "[0. 2. 0. 2. 0. 3. 0. 0. 5. 2. 0. 3. 0. 3. 5. 3. 4. 5. 4. 2. 4. 2. 0. 3.\n",
            " 1. 3. 1. 5. 1. 5. 0. 5.]\n",
            "[2. 3. 2. 3. 2. 3. 2. 3. 2. 2. 3. 2. 2. 2. 2. 2. 5. 2. 5. 4. 4. 0. 2. 0.\n",
            " 3. 0. 0. 0. 0. 3. 4. 3.]\n",
            "[2. 0. 1. 0. 2. 3. 2. 3. 2. 1. 2. 5. 2. 5. 0. 5. 4. 4. 4. 4. 2. 2. 0. 2.\n",
            " 2. 2. 2. 5. 2. 0. 3. 0.]\n",
            "[0. 0. 0. 2. 1. 2. 5. 1. 5. 1. 5. 3. 5. 3. 1. 3. 1. 2. 1. 5. 2. 5. 0. 2.\n",
            " 0. 2. 3. 2. 1. 2. 1. 2.]\n",
            "[0. 5. 5. 5. 0. 2. 0. 0. 0. 3. 0. 3. 0. 3. 0. 3. 0. 3. 3. 2. 5. 5. 5. 5.\n",
            " 5. 3. 1. 3. 1. 1. 0. 5.]\n",
            "[5. 4. 5. 1. 2. 1. 4. 2. 4. 2. 1. 4. 1. 5. 4. 4. 5. 4. 0. 2. 0. 2. 0. 3.\n",
            " 0. 3. 0. 3. 0. 3. 5. 2.]\n",
            "[5. 5. 5. 4. 2. 5. 2. 2. 2. 3. 1. 1. 1. 0. 0. 5. 3. 4. 3. 1. 0. 2. 0. 5.\n",
            " 3. 5. 3. 2. 0. 1. 0. 5.]\n",
            "[0. 3. 1. 3. 1. 5. 1. 5. 3. 5. 3. 3. 4. 3. 5. 3. 5. 3. 0. 3. 0. 3. 0. 1.\n",
            " 5. 0. 3. 1. 4. 4. 5. 1.]\n",
            "[5. 0. 5. 0. 5. 0. 2. 0. 2. 0. 5. 2. 5. 2. 2. 0. 4. 5. 1. 3. 1. 3. 4. 3.\n",
            " 2. 3. 2. 3. 1. 5. 5. 1.]\n",
            "[2. 0. 0. 0. 5. 0. 5. 0. 5. 0. 1. 1. 2. 5. 1. 4. 1. 4. 4. 4. 4. 4. 3. 4.\n",
            " 2. 4. 0. 4. 0. 4. 2. 4.]\n",
            "[5. 1. 0. 1. 3. 4. 3. 5. 3. 4. 0. 4. 0. 4. 1. 0. 1. 5. 2. 5. 1. 5. 1. 5.\n",
            " 1. 4. 1. 4. 1. 5. 5. 5.]\n",
            "[0. 2. 3. 2. 2. 2. 2. 1. 5. 3. 5. 0. 1. 0. 1. 2. 4. 2. 4. 1. 5. 1. 1. 1.\n",
            " 2. 1. 0. 1. 0. 0. 0. 3.]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 67%|██████▋   | 2/3 [00:10<00:05,  5.84s/it]"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 3. 1. 3. 0. 3. 0. 3. 0. 3. 0. 3. 0. 3. 0. 3. 5. 4. 5. 5. 0. 5. 0. 5.\n",
            " 5. 5. 5. 3. 2. 4. 3. 4.]\n",
            "[3. 1. 4. 1. 0. 0. 4. 4. 4. 1. 4. 1. 4. 1. 4. 1. 4. 5. 4. 5. 4. 5. 4. 5.\n",
            " 4. 4. 5. 3. 3. 3. 3. 1.]\n",
            "[5. 4. 5. 4. 1. 0. 5. 0. 4. 0. 4. 0. 1. 5. 2. 4. 0. 5. 0. 2. 5. 1. 1. 3.\n",
            " 1. 3. 3. 4. 5. 2. 5. 2.]\n",
            "INFO:tensorflow:Assets written to: /root/acme/c7c0ad6e-0828-11ed-b5e6-0242ac1c0002/snapshots/network/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /root/acme/c7c0ad6e-0828-11ed-b5e6-0242ac1c0002/snapshots/network/assets\n"
          ]
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          "output_type": "stream",
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            "[2. 5. 3. 2. 1. 0. 1. 0. 1. 3. 4. 1. 0. 0. 4. 5. 4. 0. 3. 0. 4. 1. 5. 1.\n",
            " 3. 2. 4. 0. 4. 1. 5. 1.]\n",
            "[3. 3. 3. 0. 3. 0. 3. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 3. 3. 3. 3. 3. 1. 5.\n",
            " 2. 5. 2. 5. 2. 5. 2. 3.]\n",
            "[0. 5. 1. 5. 1. 2. 1. 2. 3. 2. 3. 5. 3. 5. 4. 1. 0. 5. 0. 2. 5. 2. 1. 2.\n",
            " 1. 5. 0. 3. 0. 3. 0. 5.]\n",
            "[5. 3. 0. 3. 1. 5. 1. 4. 4. 2. 5. 1. 3. 0. 1. 0. 4. 0. 3. 4. 3. 4. 3. 2.\n",
            " 5. 2. 5. 5. 2. 5. 2. 5.]\n",
            "[2. 2. 0. 4. 0. 0. 0. 0. 0. 5. 2. 5. 2. 5. 2. 3. 2. 3. 5. 3. 5. 3. 5. 3.\n",
            " 5. 0. 3. 4. 3. 1. 1. 1.]\n",
            "[0. 2. 1. 0. 1. 2. 1. 2. 0. 2. 0. 2. 4. 2. 4. 3. 0. 1. 0. 2. 4. 2. 4. 5.\n",
            " 4. 4. 2. 4. 2. 0. 0. 3.]\n",
            "[2. 4. 1. 4. 1. 4. 5. 2. 2. 1. 5. 3. 0. 4. 4. 2. 4. 0. 4. 5. 4. 3. 0. 3.\n",
            " 2. 5. 3. 5. 3. 5. 4. 0.]\n",
            "[3. 4. 3. 4. 3. 4. 2. 5. 4. 5. 5. 5. 5. 3. 2. 5. 0. 5. 0. 5. 2. 0. 3. 5.\n",
            " 1. 2. 5. 2. 3. 2. 3. 1.]\n",
            "[2. 4. 2. 5. 2. 2. 3. 1. 3. 1. 3. 1. 5. 1. 5. 1. 1. 2. 4. 2. 4. 2. 3. 2.\n",
            " 3. 0. 4. 0. 4. 0. 4. 0.]\n",
            "[1. 0. 3. 1. 0. 1. 5. 1. 5. 4. 1. 4. 5. 4. 3. 2. 3. 5. 4. 5. 0. 4. 0. 4.\n",
            " 4. 4. 1. 4. 1. 4. 1. 4.]\n",
            "[4. 2. 2. 2. 1. 2. 1. 2. 0. 0. 5. 3. 5. 3. 2. 3. 2. 2. 2. 0. 3. 5. 4. 3.\n",
            " 0. 3. 0. 1. 0. 3. 4. 3.]\n",
            "[1. 3. 0. 0. 0. 3. 0. 5. 0. 5. 2. 0. 2. 0. 0. 1. 1. 2. 1. 5. 1. 5. 1. 5.\n",
            " 1. 2. 1. 4. 1. 1. 0. 5.]\n",
            "[0. 4. 1. 4. 1. 4. 1. 5. 4. 4. 1. 5. 5. 5. 5. 4. 5. 4. 5. 2. 2. 2. 2. 2.\n",
            " 2. 2. 2. 2. 1. 0. 0. 1.]\n",
            "[1. 4. 4. 4. 1. 5. 5. 5. 1. 5. 1. 0. 1. 0. 1. 0. 4. 4. 0. 0. 0. 0. 0. 0.\n",
            " 0. 2. 0. 5. 5. 5. 2. 5.]\n",
            "[3. 4. 4. 4. 4. 5. 1. 2. 2. 2. 2. 2. 1. 2. 5. 2. 2. 4. 2. 5. 2. 5. 2. 5.\n",
            " 0. 5. 0. 5. 0. 4. 0. 4.]\n",
            "[1. 5. 1. 5. 5. 0. 5. 2. 5. 3. 5. 2. 5. 2. 5. 2. 5. 2. 0. 0. 0. 0. 0. 3.\n",
            " 0. 2. 0. 2. 4. 1. 5. 1.]\n",
            "[4. 4. 2. 1. 3. 5. 0. 1. 0. 1. 4. 1. 1. 4. 1. 2. 0. 0. 5. 0. 5. 0. 2. 5.\n",
            " 2. 4. 5. 0. 3. 5. 3. 2.]\n",
            "[0. 2. 0. 2. 0. 4. 4. 2. 5. 2. 5. 2. 3. 2. 3. 4. 2. 1. 5. 5. 0. 1. 0. 1.\n",
            " 3. 1. 3. 3. 3. 3. 3. 3.]\n",
            "[5. 5. 5. 4. 5. 4. 5. 0. 5. 0. 4. 4. 2. 1. 2. 3. 4. 0. 1. 2. 1. 4. 2. 4.\n",
            " 2. 4. 2. 5. 3. 5. 3. 4.]\n",
            "[3. 0. 3. 0. 3. 0. 3. 2. 2. 5. 2. 0. 5. 0. 5. 0. 5. 4. 5. 4. 5. 4. 5. 4.\n",
            " 2. 4. 3. 4. 5. 3. 5. 0.]\n",
            "[5. 3. 1. 2. 0. 2. 0. 3. 2. 3. 0. 3. 0. 4. 2. 1. 2. 2. 2. 0. 1. 0. 4. 2.\n",
            " 2. 5. 2. 2. 0. 2. 1. 3.]\n",
            "[1. 4. 1. 1. 1. 0. 1. 0. 1. 0. 5. 4. 5. 0. 2. 0. 2. 0. 3. 1. 2. 5. 5. 5.\n",
            " 5. 5. 3. 5. 3. 2. 3. 5.]\n",
            "[3. 4. 5. 4. 4. 4. 2. 0. 1. 1. 3. 1. 1. 5. 2. 3. 2. 5. 2. 5. 2. 5. 1. 1.\n",
            " 1. 3. 2. 3. 4. 3. 5. 3.]\n",
            "[0. 2. 2. 2. 2. 3. 2. 5. 2. 5. 2. 2. 2. 5. 2. 5. 4. 1. 4. 1. 3. 3. 4. 3.\n",
            " 4. 3. 1. 4. 1. 4. 1. 5.]\n",
            "[3. 4. 0. 3. 0. 3. 0. 1. 0. 1. 3. 3. 3. 1. 0. 1. 5. 4. 5. 4. 4. 4. 4. 1.\n",
            " 2. 5. 5. 0. 4. 2. 1. 3.]\n",
            "[0. 1. 5. 1. 5. 2. 2. 3. 2. 3. 2. 3. 4. 3. 4. 3. 5. 3. 0. 2. 3. 3. 1. 3.\n",
            " 1. 3. 1. 3. 1. 3. 5. 5.]\n",
            "[2. 1. 2. 1. 2. 0. 2. 0. 2. 0. 2. 0. 2. 0. 0. 0. 2. 2. 2. 2. 2. 2. 5. 5.\n",
            " 5. 5. 5. 0. 5. 0. 2. 0.]\n",
            "[5. 0. 3. 0. 2. 5. 5. 5. 4. 5. 4. 5. 4. 0. 4. 0. 4. 1. 4. 3. 4. 0. 3. 3.\n",
            " 4. 1. 0. 4. 5. 4. 5. 5.]\n",
            "[3. 4. 5. 4. 2. 4. 2. 3. 0. 2. 0. 5. 0. 5. 4. 2. 3. 3. 3. 5. 3. 5. 2. 0.\n",
            " 1. 0. 3. 5. 5. 5. 3. 5.]\n",
            "[0. 2. 3. 5. 0. 5. 0. 5. 1. 0. 1. 0. 1. 0. 5. 0. 4. 4. 4. 5. 4. 5. 4. 5.\n",
            " 4. 0. 4. 4. 2. 4. 2. 5.]\n",
            "[1. 0. 2. 0. 5. 0. 3. 0. 3. 0. 5. 0. 2. 0. 2. 0. 1. 2. 1. 5. 2. 0. 2. 1.\n",
            " 2. 1. 1. 3. 5. 3. 5. 1.]\n",
            "[4. 0. 5. 4. 5. 1. 5. 1. 5. 0. 4. 1. 4. 1. 4. 1. 4. 1. 3. 3. 1. 3. 2. 2.\n",
            " 2. 3. 2. 3. 0. 3. 2. 5.]\n",
            "[5. 3. 5. 3. 5. 1. 0. 1. 3. 1. 3. 1. 1. 1. 3. 4. 4. 4. 4. 2. 5. 0. 5. 1.\n",
            " 5. 1. 3. 1. 3. 0. 3. 1.]\n",
            "[4. 4. 1. 0. 1. 0. 2. 1. 2. 3. 2. 3. 2. 3. 4. 4. 4. 4. 1. 0. 1. 2. 1. 2.\n",
            " 0. 5. 0. 2. 0. 5. 0. 4.]\n",
            "[1. 0. 0. 0. 1. 0. 3. 0. 3. 0. 2. 0. 3. 0. 5. 0. 5. 0. 3. 0. 1. 0. 1. 3.\n",
            " 1. 5. 1. 5. 1. 5. 5. 4.]\n",
            "[4. 2. 4. 2. 4. 2. 2. 4. 2. 2. 4. 2. 4. 2. 1. 3. 4. 3. 4. 3. 4. 3. 4. 1.\n",
            " 4. 1. 4. 1. 4. 1. 4. 0.]\n",
            "[4. 1. 2. 1. 2. 4. 2. 4. 2. 2. 2. 2. 3. 2. 3. 1. 2. 2. 1. 3. 1. 4. 3. 5.\n",
            " 3. 4. 1. 4. 3. 4. 0. 4.]\n",
            "[2. 0. 2. 2. 1. 3. 1. 3. 1. 3. 1. 3. 2. 3. 2. 3. 2. 2. 0. 2. 0. 3. 3. 3.\n",
            " 1. 3. 2. 2. 2. 4. 2. 1.]\n",
            "[4. 5. 0. 5. 0. 5. 0. 5. 0. 5. 0. 5. 0. 3. 0. 3. 0. 5. 0. 5. 1. 5. 0. 5.\n",
            " 0. 4. 0. 4. 4. 4. 4. 4.]\n",
            "[0. 2. 0. 4. 5. 5. 5. 5. 4. 5. 4. 5. 4. 5. 3. 5. 1. 4. 1. 4. 1. 1. 4. 3.\n",
            " 4. 0. 2. 3. 3. 3. 3. 3.]\n",
            "[4. 3. 0. 3. 5. 4. 3. 3. 0. 0. 1. 0. 2. 0. 2. 4. 4. 4. 4. 2. 4. 1. 4. 1.\n",
            " 1. 1. 1. 1. 4. 3. 1. 3.]\n",
            "[4. 4. 4. 4. 1. 2. 1. 2. 5. 2. 1. 5. 3. 4. 3. 1. 0. 2. 0. 1. 0. 1. 5. 1.\n",
            " 2. 2. 3. 2. 1. 1. 4. 0.]\n",
            "[5. 5. 4. 5. 4. 4. 1. 5. 4. 4. 5. 4. 0. 4. 0. 5. 0. 5. 0. 4. 0. 2. 5. 2.\n",
            " 5. 3. 2. 3. 5. 3. 1. 3.]\n",
            "[4. 2. 5. 2. 5. 0. 5. 1. 5. 1. 3. 5. 1. 3. 1. 3. 1. 3. 0. 0. 0. 3. 2. 2.\n",
            " 2. 2. 0. 0. 5. 0. 5. 2.]\n",
            "[1. 1. 1. 5. 1. 5. 3. 5. 3. 5. 3. 5. 3. 2. 3. 3. 3. 2. 3. 4. 3. 1. 3. 0.\n",
            " 3. 0. 4. 2. 4. 4. 4. 1.]\n",
            "[4. 2. 4. 2. 2. 2. 2. 2. 1. 3. 1. 0. 0. 0. 3. 0. 3. 5. 3. 0. 3. 0. 3. 4.\n",
            " 5. 0. 5. 0. 5. 0. 1. 4.]\n",
            "[1. 3. 1. 2. 1. 2. 1. 2. 1. 2. 1. 0. 1. 4. 1. 4. 2. 5. 1. 4. 1. 4. 2. 3.\n",
            " 3. 3. 2. 4. 2. 4. 4. 1.]\n",
            "[1. 5. 1. 5. 1. 5. 1. 2. 3. 0. 5. 5. 0. 5. 4. 2. 4. 4. 4. 4. 4. 1. 4. 1.\n",
            " 4. 1. 0. 1. 3. 2. 1. 2.]\n",
            "[2. 5. 1. 4. 3. 3. 0. 0. 1. 5. 1. 5. 1. 4. 1. 5. 1. 5. 1. 2. 5. 2. 5. 2.\n",
            " 5. 4. 5. 4. 5. 4. 5. 3.]\n",
            "[4. 3. 4. 1. 5. 1. 5. 5. 1. 0. 1. 0. 1. 0. 2. 0. 1. 0. 1. 0. 1. 0. 1. 0.\n",
            " 3. 0. 3. 2. 3. 3. 4. 3.]\n",
            "[4. 0. 0. 0. 2. 0. 1. 1. 0. 0. 0. 4. 0. 2. 0. 2. 2. 2. 3. 2. 3. 3. 5. 3.\n",
            " 5. 3. 3. 2. 4. 4. 4. 0.]\n",
            "[4. 3. 1. 3. 1. 3. 1. 1. 1. 1. 2. 1. 2. 3. 2. 3. 3. 3. 3. 5. 3. 5. 3. 5.\n",
            " 0. 5. 0. 4. 5. 5. 5. 5.]\n",
            "[2. 2. 0. 2. 0. 2. 5. 2. 1. 2. 1. 0. 1. 1. 1. 0. 4. 4. 4. 4. 4. 4. 4. 4.\n",
            " 2. 4. 1. 4. 1. 2. 2. 2.]\n",
            "[3. 2. 4. 2. 4. 5. 4. 5. 3. 5. 3. 1. 3. 2. 3. 1. 2. 1. 2. 1. 4. 0. 4. 4.\n",
            " 3. 4. 4. 1. 4. 0. 4. 2.]\n",
            "[3. 0. 1. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 5. 1. 2. 4. 5. 4. 5. 3. 5. 3. 2.\n",
            " 2. 2. 2. 1. 2. 2. 2. 2.]\n",
            "[4. 4. 0. 2. 0. 2. 4. 2. 0. 3. 4. 3. 4. 2. 4. 2. 4. 2. 3. 2. 3. 2. 3. 3.\n",
            " 1. 1. 1. 1. 1. 4. 2. 4.]\n",
            "[0. 2. 0. 2. 5. 2. 3. 1. 5. 1. 5. 1. 5. 0. 5. 0. 0. 0. 4. 4. 4. 4. 1. 4.\n",
            " 5. 4. 1. 4. 0. 4. 0. 2.]\n",
            "[0. 4. 4. 4. 0. 4. 2. 3. 2. 4. 3. 2. 3. 1. 3. 5. 3. 5. 3. 4. 1. 3. 1. 3.\n",
            " 1. 4. 4. 4. 4. 2. 4. 1.]\n",
            "[1. 2. 2. 1. 0. 1. 0. 1. 0. 0. 0. 3. 0. 5. 0. 4. 1. 4. 1. 5. 4. 3. 2. 3.\n",
            " 3. 0. 3. 0. 3. 4. 0. 1.]\n",
            "[4. 0. 2. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 5. 3. 5. 3. 4. 0. 4. 0.\n",
            " 3. 4. 1. 4. 1. 0. 2. 0.]\n",
            "[3. 4. 3. 4. 5. 1. 5. 1. 5. 3. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 5. 4. 2. 4.\n",
            " 2. 4. 2. 1. 2. 1. 4. 1.]\n",
            "[3. 1. 4. 1. 1. 0. 4. 4. 4. 4. 4. 4. 2. 3. 4. 3. 5. 0. 5. 5. 2. 5. 5. 2.\n",
            " 0. 2. 0. 2. 0. 5. 0. 1.]\n",
            "[2. 0. 1. 0. 2. 0. 2. 4. 2. 4. 5. 5. 5. 1. 5. 0. 5. 0. 5. 0. 4. 0. 1. 3.\n",
            " 5. 3. 4. 3. 4. 3. 2. 3.]\n",
            "[0. 5. 4. 1. 4. 1. 1. 0. 2. 0. 5. 0. 5. 0. 5. 2. 4. 0. 4. 0. 1. 5. 0. 5.\n",
            " 3. 0. 3. 0. 4. 1. 4. 0.]\n",
            "[0. 2. 0. 4. 0. 4. 0. 0. 0. 0. 0. 0. 2. 0. 5. 0. 5. 2. 5. 2. 3. 2. 4. 4.\n",
            " 3. 4. 1. 4. 5. 3. 0. 4.]\n",
            "[5. 3. 2. 3. 2. 3. 2. 3. 3. 2. 5. 2. 0. 2. 0. 2. 0. 0. 0. 1. 4. 1. 4. 5.\n",
            " 0. 1. 2. 1. 3. 5. 5. 5.]\n",
            "[0. 4. 5. 4. 1. 0. 0. 0. 0. 0. 2. 4. 0. 4. 2. 3. 2. 2. 3. 2. 3. 5. 3. 3.\n",
            " 5. 3. 5. 2. 4. 2. 4. 2.]\n",
            "[4. 0. 1. 1. 1. 1. 1. 0. 1. 2. 5. 2. 5. 2. 5. 2. 3. 1. 3. 1. 3. 2. 3. 2.\n",
            " 3. 2. 4. 1. 4. 1. 4. 4.]\n",
            "[4. 3. 4. 1. 3. 3. 3. 3. 2. 3. 1. 0. 1. 0. 3. 3. 3. 2. 2. 4. 5. 3. 5. 4.\n",
            " 3. 4. 4. 4. 5. 4. 5. 4.]\n",
            "[3. 0. 0. 3. 0. 3. 4. 3. 4. 2. 0. 4. 4. 3. 4. 2. 5. 5. 1. 2. 3. 5. 4. 0.\n",
            " 1. 0. 2. 0. 2. 5. 2. 5.]\n",
            "[4. 2. 4. 5. 3. 3. 2. 3. 5. 3. 5. 4. 5. 1. 5. 1. 5. 3. 0. 2. 5. 0. 0. 1.\n",
            " 4. 2. 4. 1. 1. 3. 1. 1.]\n",
            "[2. 3. 0. 3. 2. 4. 2. 4. 2. 0. 4. 0. 5. 0. 4. 0. 4. 5. 3. 3. 0. 5. 0. 5.\n",
            " 1. 5. 4. 0. 3. 0. 3. 0.]\n",
            "[3. 5. 4. 4. 2. 4. 2. 4. 2. 4. 5. 1. 5. 1. 5. 1. 1. 2. 3. 1. 3. 1. 3. 0.\n",
            " 3. 0. 3. 1. 5. 3. 5. 4.]\n",
            "[5. 2. 5. 1. 1. 4. 2. 4. 2. 0. 4. 3. 4. 3. 4. 3. 1. 2. 1. 2. 1. 2. 2. 4.\n",
            " 5. 0. 0. 0. 0. 0. 0. 1.]\n",
            "[4. 0. 2. 2. 0. 2. 0. 5. 0. 5. 0. 4. 5. 4. 5. 4. 3. 4. 3. 2. 3. 1. 3. 5.\n",
            " 4. 5. 0. 5. 4. 5. 0. 4.]\n",
            "[5. 2. 5. 3. 1. 3. 1. 4. 3. 0. 4. 0. 4. 0. 4. 0. 3. 1. 2. 1. 2. 5. 2. 1.\n",
            " 2. 1. 2. 5. 2. 5. 5. 3.]\n",
            "[5. 3. 0. 3. 1. 3. 1. 0. 1. 1. 1. 1. 4. 4. 4. 0. 1. 2. 3. 2. 0. 5. 4. 1.\n",
            " 1. 0. 1. 3. 5. 1. 3. 5.]\n",
            "[3. 5. 4. 5. 4. 5. 4. 5. 1. 5. 5. 4. 1. 5. 3. 5. 5. 5. 1. 5. 4. 1. 3. 1.\n",
            " 3. 1. 5. 0. 5. 4. 5. 4.]\n",
            "[5. 3. 4. 3. 1. 3. 1. 2. 1. 0. 5. 1. 3. 1. 3. 1. 1. 4. 1. 2. 4. 2. 5. 2.\n",
            " 0. 3. 4. 3. 2. 3. 1. 2.]\n",
            "[0. 4. 5. 5. 1. 1. 1. 4. 1. 4. 0. 4. 4. 1. 5. 1. 5. 3. 2. 3. 2. 5. 2. 0.\n",
            " 0. 0. 0. 3. 0. 0. 1. 4.]\n",
            "[5. 3. 0. 3. 1. 1. 1. 3. 1. 3. 0. 2. 5. 2. 0. 2. 4. 2. 0. 1. 5. 0. 5. 3.\n",
            " 5. 3. 2. 5. 4. 0. 3. 4.]\n",
            "[1. 3. 1. 4. 1. 3. 1. 3. 1. 3. 1. 3. 4. 0. 1. 0. 5. 0. 5. 5. 2. 5. 5. 0.\n",
            " 4. 3. 3. 3. 3. 1. 0. 2.]\n",
            "[4. 2. 3. 0. 3. 0. 1. 4. 3. 4. 3. 4. 4. 4. 3. 3. 1. 0. 1. 3. 1. 3. 1. 0.\n",
            " 1. 3. 1. 2. 1. 2. 2. 3.]\n",
            "[4. 1. 0. 1. 2. 1. 4. 1. 5. 0. 1. 0. 5. 3. 5. 4. 1. 4. 2. 4. 4. 1. 3. 5.\n",
            " 3. 5. 3. 3. 3. 3. 3. 2.]\n",
            "[2. 2. 2. 2. 0. 1. 0. 1. 5. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 5. 4. 3.\n",
            " 4. 3. 2. 3. 2. 3. 2. 3.]\n",
            "[4. 4. 4. 4. 4. 4. 5. 4. 1. 0. 5. 0. 1. 2. 3. 5. 0. 5. 1. 1. 1. 2. 1. 2.\n",
            " 1. 3. 2. 0. 2. 0. 3. 0.]\n",
            "[0. 4. 0. 4. 4. 2. 4. 2. 4. 2. 4. 1. 4. 1. 4. 1. 1. 1. 1. 1. 3. 1. 2. 5.\n",
            " 2. 5. 5. 3. 3. 5. 3. 5.]\n",
            "[0. 5. 0. 0. 0. 2. 1. 2. 1. 2. 1. 2. 1. 2. 3. 5. 3. 5. 3. 5. 3. 1. 3. 4.\n",
            " 3. 0. 3. 2. 4. 2. 5. 2.]\n",
            "[4. 0. 2. 4. 3. 2. 3. 2. 3. 2. 1. 2. 1. 1. 1. 1. 1. 1. 1. 4. 1. 5. 3. 2.\n",
            " 0. 2. 0. 1. 0. 3. 2. 3.]\n",
            "[4. 1. 2. 2. 2. 2. 2. 4. 2. 4. 2. 4. 2. 4. 2. 4. 1. 4. 4. 4. 2. 3. 2. 1.\n",
            " 2. 3. 2. 4. 3. 0. 3. 4.]\n",
            "[5. 2. 5. 2. 5. 2. 5. 2. 5. 2. 5. 2. 5. 2. 5. 2. 4. 2. 1. 0. 1. 2. 3. 2.\n",
            " 3. 3. 4. 1. 4. 1. 1. 3.]\n",
            "[1. 5. 3. 5. 1. 5. 1. 5. 1. 5. 0. 5. 5. 5. 5. 2. 4. 5. 4. 5. 1. 5. 1. 2.\n",
            " 2. 3. 2. 3. 5. 3. 5. 2.]\n",
            "[0. 1. 0. 1. 0. 2. 5. 2. 5. 2. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n",
            " 0. 2. 0. 5. 5. 5. 4. 5.]\n",
            "[3. 0. 1. 0. 5. 5. 5. 5. 2. 1. 5. 1. 0. 1. 1. 5. 1. 5. 4. 5. 2. 5. 2. 5.\n",
            " 5. 1. 5. 1. 3. 1. 0. 0.]\n",
            "[1. 3. 1. 3. 5. 3. 5. 3. 3. 2. 3. 2. 1. 4. 5. 5. 5. 3. 4. 2. 5. 2. 3. 5.\n",
            " 4. 2. 2. 3. 2. 5. 2. 2.]\n",
            "[4. 5. 5. 5. 5. 5. 2. 0. 1. 2. 2. 2. 2. 0. 4. 4. 1. 4. 1. 4. 4. 4. 4. 4.\n",
            " 4. 0. 1. 4. 3. 4. 1. 4.]\n",
            "[0. 2. 5. 3. 4. 2. 3. 2. 0. 2. 0. 2. 0. 2. 0. 3. 5. 5. 0. 4. 2. 0. 2. 0.\n",
            " 2. 3. 3. 3. 3. 3. 5. 3.]\n",
            "[1. 5. 3. 2. 3. 2. 5. 0. 5. 0. 5. 0. 1. 0. 4. 0. 1. 1. 1. 1. 0. 1. 0. 1.\n",
            " 2. 1. 3. 3. 3. 1. 0. 3.]\n",
            "[5. 1. 5. 1. 5. 2. 4. 4. 1. 2. 1. 5. 4. 5. 3. 2. 3. 2. 1. 2. 5. 2. 0. 2.\n",
            " 1. 2. 1. 2. 0. 2. 0. 2.]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3/3 [00:17<00:00,  5.90s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_reward_by_episode(logs_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "zAS4HBUtHY_Z",
        "outputId": "e19376db-f248-43ba-9eed-037b115dde7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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WE4FDp1BZKwKHIAgdm81bh4pKrGhxtK9YfTRWjYTHViNyOQRB6NBOTMUVLY52FhcRh1UrE6VYsdg97V0dQRCEZtW6LUB4kv9ABI6QmSOTUSUJs8EiBsgFQejQglnjosXRvuJi0gAwGaxinEMQhA7N4rYiSzLR+siwXF8EjhDFmRIBMOjtosUhCEKHVuu2EK2L5rWFu7E5Wr9rXQSOEJ2YD60zukTgEAShQ7O4rRgwsWlvOWXVrd9DIgJHiKL1UcgqqHqPyOUQBKFDq/VY0akmABLMxla/vggcIZIlmRhZh0sjkgAFQejYLG4LeCPQyBLmKH2rX18EjhaI0Zqo08r4rNUoisjlEASh43H7PTh9LvxuPXHRBmRJavUyROBogVh9DBaNTKxko7bO3d7VEQRBaMRSn8PhduhIiGn9biroIIFj4cKFXHPNNQwaNIi5c+c2OOZ0Orn//vu54oormDRpEitWrGinWkKsKR6rViZeFjOrBEHomE7kcNhtGuLP58CRlZXF7NmzmTx5cqNjb775JlFRUXzzzTe89tpr/OlPf8Jut7dDLSE2MhmXRsassYpxDkEQOqQTWeM2ixyWgXHoIIGjf//+9O3bF1luXJ2vv/6an/3sZwD07NmT7Oxsvvvuu7auIgCxEfEAmIw2sROgIAgd0ol1qhS3kYQYQ1jK6BCB43SKi4vJyMgI/p6WlkZpaWm71OVELofR6BRdVYIgdEgWtxW9rAdFG7YxDm1Yrvoj06ZNo7i4uMlja9euRaPRhL0OCQlRZ/3cpKRoADyGdNgKst6F1ekNPn6+Ot/vryld8Z6ha973+XrPzv0OTJpoLEDfngmN7rM17rtNAseCBQvO+rnp6ekUFRURHx/oJiopKWHkyJEtvk5VVd1ZTaFNSoqmosIGgN8XCHAejZeSCmvw8fPRqffdVXTFe4aued/n8z2XWavQ+AMtDcnnb3CfLblvWZaa/cLd4buqJk2axIcffgjA0aNH2bFjB2PHjm2XukRojRgkDTathFJXg19R2qUegiAIzbF4rEi+CKIidBj04enN6RCB48svv2TcuHEsXryYF198kXHjxnHw4EEAZs6cidVq5YorruDOO+/kb3/7G1FRZ9/tdK7M2kis2kAuh6VO7MshCELHoagKFrcVv1tPfJgGxqGNuqrOZPLkyU1OxQUwmUy89NJLbVyj5sUazVg1lcTLdmrrPGGbJy0IgtBSdq8Dv+rHbdeREsbPpg7R4uhMzBEJWLQaEjQ2LCJ7XBCEDuRE8l+dTRO2GVUgAkeLxRpjsWll4uQ6asUWsoIgdCA1rhoAPA59WHtDROBooViDGb8kEamrEy0OQRA6lBJ7GQCqM4qEGAPew/moir/VyxGBo4VOJAHqDU5qxeC4IAgdSFFdCVGaGFC0pPhLcC17BaX8cKuXIwJHC8XWBw6/zoPF5mjn2giCIJxUbC8lSkoAwEx9voah9fcdF4GjhWINZgBsWgl/XU0710YQBCHAq/goc1Sg95nRaiQMvkDgkCPjWr0sEThaKEYfjQRYtRpw1rZ3dQRBEAAos5ejqAqqM5r4aCM4akFnRNJHtHpZInC0kEbWEKU1YdXIaN1WsROgIAgdQrE9sPir2xpJgtmI6qhBMsWGpSwROM5CrCEGq1bGLDmwOcQAuSAI7a+orgStpMFSrSM+xoBirwlLNxWcJnP8oYceQgphr9qnn366VSvUGZiNcVRqj5MpO6it82COCl9qvyAIQiiK60pJiUzmkM1LQowR9Xgtckq/sJTVbIujR48edO/ene7duxMdHc2yZcvw+/2kpqaiKArLly8nJiYmLJXq6AItDg2xskPsPS4IQodQbC8lUZ+MCsRHG1DttW3f4rjnnnuCP8+cOZM5c+aQk5MTfGzTpk28+uqrYalURxdrMOPQSERq7FhE9rggCO3M7nVQ67YwOCqw/USSyQ+KDylMgSOkMY6tW7cydOjQBo8NHTqULVu2hKVSHV1C/RayGFyixSEIQrsrrisBQOcLDIYnaAM7lLbr4PigQYN4/vnncbkClXG5XMyePZusrKywVKqjSzUlA+AyerHYROAQBKF9FdUFZlSpjkC6QIwUSE5u866qUz311FM8+OCD5OTkEBMTg9VqJTs7m2effTYsleroUiIDgaNaL2G3nZ+7iAmC0HkU20uI1JqwWiViow3IrkCOWbi6qs4YOPx+P+vXr+e///0v1dXVlJeXk5SURHp6elgq1BkYNHriNCbK9S781SJ7XBCE9lVcV0p6VCrVx931ORwVAEgmc1jKO2NXlUaj4Z///CcGg4G0tDSGDh3apYPGCanGOMr1GnCJ7HFBENqPoioU2UtJj0qjotZFotmIaq9BiohBksOzV19IYxyXXnop3377bVgq0FmlRqZSodOicdWiqCJ7XBCE9lHtqsHj95BmSqHG5ibRHIFir0EyhaebCkIc43C73dx7770MHz6c1NTUBomBXTEBECDN3A1f+WYMBht1Ti8xJn17V0kQhC7oxMB4pBSPoh4PtDgqapAi48NWZkiBo3///vTv3z9sleiMUqMzAj8Y7VjqPCJwCILQLixuCwCqywRAktmIaq9Fk9wnbGWGFDhOTQYUAlLrZ1b5I5xY6txkJke1c40EQeiK7N7A1Nu6ukCXeUK0FtVla/+uKgCPx8ORI0eoqalBPaVPf/To0WGpWEcXqTMRpUg4DV6xE6AgCO3G7nVg1BiotniRJIjVuHADUmR4kv8gxMCxadMm7r//fjweD3V1dURFRWG320lNTWX58uVhq1xHlywbsRrqsNhFEqAgCO2jzusgUmeisspFfLQBjSvQdRWu5D8IcVbVU089xe23387GjRuJjIxk48aN3H333dx8881hq1hnkKqLplovUWN1tndVBEHoouw+eyBwWJwkmCNQHIHcsnB2VYUUOI4ePcqMGTMaPHbHHXfwzjvvhKNOnUaKMQGXRsZeP6tBEAShrdm9DiJ1kVRaXPUD44HA0e4tjujoaOrq6gBISkri4MGDWK1WHA5H2CrWGaRFpQLg8Ba3c00EQeiq7F4HEZoIam2BrHHFXgsaLRgiw1ZmSIHjiiuuYNWqVQBcd911zJgxg2uvvZaJEye2SiUWLlzINddcw6BBg5g7d26DY48//jiTJk1iypQp3HTTTezYsaNVymwNaeZMAFxqZTvXRBCErsrudaBRDahAojmifsvYuJA24jtbIQ2OP/roo8GfZ86cydChQ7Hb7YwdO7ZVKpGVlcXs2bOZM2dOo2Pjxo1j1qxZ6HQ6VqxYwQMPPMCyZctapdxzZTZnYPAreLQ2VFUN63+UIAjCj/kVP06fE9WnAyAp1oh6LHxbxp4QUuDYu3cvAwcODP5+6oZOreFEcqEsN24AXXrppcGfhw0bRmlpKYqiNHluW5MjzCR7/dgMThxuH5FGXXtXSRCELsThC0zM8XsCH+UJZiOKoxZNQo+wlhtS4LjrrrtwOp2MGDGCvLw8cnNzGTRoUJt/w37//fe55JJLzipoJCScfYJeUlJ088f8MuUGH5JWe9rzOqPz7X5C0RXvGbrmfZ8P9+yx2gM/KAY0skS/ngkUOmqJHJBLQjP31xr3HVLgWLlyJYWFheTn55Ofn8/cuXOpra1lxIgRvP7662d8/rRp0ygubnoAee3atWg0mjNe46uvvuKLL77g/fffD6XKjVRV1aEoLV+MMCkpmoqK5vfcSFINuHU+dh0uxqQ9f7qqznTf56OueM/QNe/7fLnn45bA8um1NQpx0SaqSytQvW5ccmST99eS+5Zlqdkv3CFnjmdmZuLz+fB6vXg8Hr7//nuqqqpCeu6CBQtCLaZJ33zzDbNnz+add94hMTHxnK7V2hL00UANhZZycslo7+oIgtCFnFhuxGaDpNgIFEf9Bk5hzOGAEAPH/fffz9atW0lOTiYvL48pU6bw+OOPExUV/vWZVqxYwVNPPcXbb79Nt27dwl5eS8Wb4sBXQ4Vd7MshCELbqqsPHJZalR7dT+ZwhGvnvxNCGizYvXs3siwzcODA4L/WDBpffvkl48aNY/Hixbz44ouMGzeOgwcPAvDHP/4Rr9fLvffey9SpU5k6dSo1NR1n171YU6AFVOuobueaCILQ1di9gTEOq1Ui0WxEsZYDIEcnhbXckFocS5cupby8nE2bNpGfn8+cOXNwu93k5OTwj3/845wrMXnyZCZPntzksfXr15/z9cMpNjoZrOByh9ZtJwiC0FrsXgcyMigakswRKJYy0OrDusAhhNjiAEhOTqZXr1706NGDjIwMKioq+O6778JZt05BFxmPya/gUaztXRVBELoYu9eBQTYCUmAqrqUU2ZyCJIU3XSHk6bg//PADkZGR5OTkMGHCBB5++GF69uwZ1sp1BlKEmWifgl1y4FcUNB0gv0QQhK7B7nWgxQgQ6KqylKJJ6B72ckMKHFdeeSWPPvoomZmZ4a5PpyOZzET7Fep0Hix1HuJjjO1dJUEQugi7146s6NHIEuZIDQ5rBXLvvLCXG9LX42uvvZbU1FQ2bdrEokWLAHA4HF1+kUMAyRBFtF/Bp/NRZXW1d3UEQehC7F4HildHgtmIZKsCVUE2p4a93JBaHPv27ePuu+9Gr9dTVlbGT37yE/Lz81mwYAEvvPBCuOvYoUmyTLSqxaNVqKx10q9beAelBEEQTrB7Hfjc8aTVd1MByOaUsJcbUovjscce495772Xx4sVotYFYk5uby+bNm8Nauc4iRmNElaHUKnI5BEFoG6qqYvc5cDk1gQ2cgoEj/C2OkALHwYMHmTp1KkBwfSqTyYTbLbZMBYjVB9a9L7eJwCEIQtvwKl58ig+PS0NSbH2LwxCJZAx/YnZIgSMjI4OdO3c2eGz79u107x7+0fvOwKyPAaDSaWnnmgiCcLbKHRWsPL4GVW35mnbt4cRyI6pPVz8Vt6xNWhsQ4hjHfffdx5133slNN92E1+vl9ddfZ968eTzxxBPhrl+nYI6IA0chFpcIHILQWX164Et2Vu0hMyqDPrE927s6Z1QXDBz6YPKfJj2rTcoOqcVx6aWX8sYbb1BdXU1ubi5FRUW8/PLLXHzxxeGuX6dgNiUA4PGLripB6IzK7OXsrNoDwLeFnSOx+cRyI/h0JEZKqPbqNhkYhxBaHH6/n4kTJ7Jo0SIee+yxNqhS52OIjMdQpqBq7DhcPkzGkBcdFgShA1hxfA1aWUtuynDWl2yi0llNYkR88LhP8aGVO9bf9YmuKo1qINJXg5O2GRiHEFocGo0GjUYjBsJPQ4owE+NX0OicIpdDEDqZOq+d9SWbyE0ZzuTeVyJJEiuPfx88vqxgFQ+tfoyj1oJ2rGVjJwJHvCka1VoGtM1UXAixq2rGjBncf//9bNy4kYKCAgoLC4P/BJBNgWVH0LlF4BCETmZN0Qa8ipcJmWOJNZgZkTyUtcUbcfqc5JduYcHBr/D4PXy0byGKqrR3dYNOBI7EqGiU2rbL4YAQB8dPDIKvWbOmweOSJLFnz57Wr1UnI5kCLY4ivZdqETgEodPwKT5WHV/DwLh+pEcFunkmZI4lv2wLH+z9lG0Vu+gf24fc1OG8v/cT1pds5qL03HaudYDdZwe/hmRzFIqlDCkyDknXNksehRQ49u7dG+56dG66CKIV8Or8VFqc7V0bQRBCtLlsGxaPjVuybgw+1j2mG31je/FD+XbSI1P59QUziNAaWVeyiYWHFjEsKRuTLqIdax1gcdlRfDoS44woZW03FRdasKy60DxJkoiR9SgyVFg7/z7GgtAVePwevjyylIyoNAbF929wbGqfq8iK789vht6GSReBJEnc2H8qdq+Dr44sbacaN2Rx2sCnJzE2ArW2tM26qUAEjlYTozEBUOkQuRyCEE5Wj40Xt8xhZ+W5dZMvPbaSalcNN/SbElwR44Te5p7cM+x24own157LjM5gTMZIvitax2HL0XMquzVYPXZUn46kCD+qu04Ejs7IbAik+deIJEBBCBuXz82r295if81BVh5fc+Yn1PP4vbj9nuDv5XWVfFOwkhHJQ+kX1yfk60zpPYkEYxyvbX+HMkdFi+re2hxeRyBrnMBnjuiq6oTMBjMAdl8dPn/HmXkhCOcLv+LnzV1zKbQV09vcgwM1h4l+ODEAACAASURBVHD5QksTeGvX+8z6/u98c2wlXr+X/279BBmJaX2vblEdInUmfjv0diQkXtn6JhZ3+3VNuxUXGsWAoX7baimmA7Y4vF6v2I/jNMz1yUKyzkWtTeS8CEJrUlWVefvms7tqHzcNmMY1vSfiU/3sqzlwxuda3DZ2Vu7BqDXw2aFF/HXdv8gv2saknpc16IoKVZIpgd8MvQ2bx8Zr299q0JJpK4qq4MNNhNaEWt89LkfGtVn5IQWOffv2MXHiRP70pz/x6KOPApCfn8+sWbPCWrnOxGiKR6eo6PV2kcshCK1s0ZFvWFuSz6Sel3Fxxij6mHth1BjZWXnmGZ+by7eiovK7Ybfzu2G/JkofSaY5nQndx511fXrEZDJ90M8osBWxq6rtZ506fE6QIFpvQnVaQKuHNpqKC2I/jlYjR8YS4/Oj1TuptIjAIQitZU3xBhYdXcao1Bwm97oSAI2sISuhP7uq9pwxKW9T6VYyo9JJjUxhYHw/ZuU9wDMTH0V3jkuIZNXPxKpyVp/Tdc5GnSewTlWsMRrVaUGKMDca4A8nsR9HK5EjAnuPyzoXFbUil0MQWsPOyj3M27eArPj+3DzwugYfjhckZGHx2DhuK272+eWOCo7ZCslJHd7gcVk69+HdCK2RCG0E1a62X9y0yh4YW0mIjEZ1WJBM5jYtX+zH0Uokk5kYnwJ6H1WixSF0UUWVdl74eBvPf7T1nK9l89Tx5q73yYhK4/bsW9HImgbHByUMQEJiR1Xz03LzS7cgIZGTMuyc69OUeGMsNe6asFz7dEotgWCVFB2D6rQgR7Rt4BD7cbQSqb7F4dN5qawWgUM4/6meQMta0kdQ5/Sy4LvDrNpajKqqqEBptYPUeNNZX/+YtRCP38N1fa/BqG3cfx+tj6JnTCa7Kvdyda8rGtdPVdlUtpV+cX2INYTngzXOENsuLY4KW2BAPM0ch+KwoEkb2Kbld4j9OBYuXMg111zDoEGDmDt3bpPnbNiwgaysrGaPtzdJqyda1eCTVZE9Lpz3VL8Px8IncHz2BKrfx3tL9rFqazGXXpjBn36RA8D2g5XnVEZRXQkA3aLTmj0nOzGLY7bCJqfFFtiOU+6sJDdMrQ2AeGMcNe3RVeUI3G96TBS47W3eVRXy6NCgQYPCth9HVlYWs2fPZs6cOU0er6ur49lnn2XcuLOfBdEWYjRGQKXWbcXnV9BqRJqMcH7y7lyKUhMYW/DsWMregmhGDU7hlisCA8YZiZFsO1TFlXln351dZD1OnGzEeJqx7+yELL44vIRPD3xOiimpwbGDlqNoJQ3Dki4IqbyCMhuF5XVclJ0a8kBzvDEWh8+Jy+dqslUULlaPDVWRiZP9OAn0eLSlZgPHiy++GNIF7rvvvnOuRP/+gTebLDf9QfvPf/6TmTNnsnLlynMuK5zMukigDrRuqm1ukmPbfyE0QWhtir0G9w+fo+k+FAD3DwuRnNfQJ6N38JwhfRJYml+I0+0jwtCy2Uuq4sO7ewXHS7aT4vHi2fkNhgunNHluRlQamVHpbC7f1uTxi9LyzrggYY3NzYLvDrNmRwkqUGVxMeXiXiHV9UQeSLWrNri6bluo89qQVSOSK9DykDtKi6O0tDT4s9vtZunSpWRnZ5ORkUFxcTE7duzgyiuvDHsFV61ahc1mY9KkSR0+cEQbYoA60HmoqnWKwCGcl9wbPgK/D+Pom0GSsH34R6aYfqBP+nhUrwvPtq+52FvL10p3dh2pJmdgcsjXVlw2nJ8/hae2mIo+yQzymfAdXI9++DVNtgIkSeKRvPvP+l427S3nza/24PMrTMzrjsXu4bPvjxAXbWDs0PQzPj++PnDUuNs2cDgVOzpMqE4r0IFaHE899VTw5wceeIDnnnuOiRMnBh9bunQpixcvDqmQadOmUVzc9JS5tWvXotFomjxmtVp57rnnePvtt0Mq53QSEqLO+rlJSdEhnWeLSwJLMZLOjVsJ/XkdVWev/9noavesKn4Un4eEOAMSEpJWd9rzXYV7sB1cR+xF1xLfty8AO+IvIrdqNTEVK7Av+xa/vZYoYGTkBPYVdeeqsaGvBWXZtAZ7bTH+q2aiHPiCvj2Ho6xZhFmpxJB6skWjKn5Uxd/kNWStPqSyjJEG3lu6n24pUTw8PZe0xEh8foW/vbGe/y7ZR/eMWHKyTr+MhyYyEzaDR+ts0/eOT3ISrUkgUuPGCSR2S0drDq381qhnSG3I7777jmeffbbBYxMmTOCPf/xjSIUsWLCg5TUD9u/fT0VFBTfccAMANTU1rFixgtraWu65554WXauqqg5FUVtch6SkaCoqQhvs1kgxSKqKTufm8PFaKnrHn/lJHVRL7vt80dXuWbGU4vj8yeC3VgDDuF+hHzi+yfNVVcXx9ZtIkfH4BkwMvlZfWrL4lbQF1s5HTulLxGW/xf39u0yt3cRzuzIpK++LHOKYgWP3BqSYFA5pA7OxktNyQFpCef5yjKMCYxiqqw77Z39DtZY3eQ1t71yME+5Ckpv+QgqB/+s3FmynzunhD1cMRasqwfu5/eos/vXBD/zzv/n8383D6ZUW0+x1FFVClmQKKkupMLfde8evcWKUTFjLA1vGVjs1SJ4zl9+S97gsS81+4Q4pcPTo0YP333+fGTNmBB/73//+F/Y8jpycHNatWxf8/ZFHHiE7O5tbb701rOWeLV1kLKZKFdXkokps6CR0cK61H6D6PMRdcgt2hxvfgfV4tnyJrv9YpCbGG/2l+1EqjmC4eAaSzgCA2+vnSIWbHcNu5rKBJjTdhwa6lMbcivLFU+SpWzhaMoLe6c1/+J6get34i3ejy7qUEnsZGklDSmwP3JkX4Du4HjXvRiRZxp3/KaqtEv2In4Km4UeYWleNd/e3uA2RGC7+RbOD3EeKLazYUsSlwzPontLwG3iEQcv9NwzlH+9u5sWPtzFrRk6z3c6yJBNnMLfpzCqbywkaHzHaQPIfhkgkzelbiq0tpMDx97//nXvuuYc33niDlJQUSktL0el0vPzyy61SiS+//JKnn34aq9XK8uXLmTNnDm+99RZ965vCnYUUYSbKr+AxuqmoEbkcQsflO7YVf+F2DKN+RtyYa/FV2JBjUnAtewXfsS3oeo1o9BzvjiVIhih0/ccEHztaYkVRVVL69EfbIzH4uDZtAPTM47Ijm9i4+wC90xtf78f8xbvB70PbfRhFletJjUxGI2vQ9RuNq2Ar/tJ9SPoIvHtWosu+HMOInzZ5HUlnxLNtEVJUAobh1zQ6rqoqry/YQaRRx0/H9m7iChAbZeD3PxvKk+9tZvaHW5k1fQTRpqa7wOKMbZvLUVRbVV+uGdVS0ubJfxBi4Bg0aBBLlixh27ZtlJeXk5SUxLBhw9DpWifKTZ48mcmTJ5/xvH/+85+tUl64SFFxRPkVqnXuDp89rljK8B7ZjD77cqQQ+4Sbs/VgJVqNRHavhFaqnRBOqs+Da+37yLFp6AafTJzT9rwQKToR744ljQKHYinDd3QL+uGTkbSG4OOHigPdXH2aaFFEjvk5NUe3kHTwMz5Qo0CSSIkzMeHCjCZbAr6CbaAzokkbQPGxz+gXGxgb0fYYBjojvgNr8deWIBmjmg0aAPq861HsNXjyP0WxlCPpjVRbXexxp1AW2Z86h5ddh6v4xaQBREU0/xmWlhDJvdcP4Zn/beWlT7bz8C0XNjnFPt4Yx4Gaw81ep7WVWAOZ6kmRsaglbb/cCLQgj0OSJFRVRVGU4O9CQ3JkPFF+hXKjh1qbG69PQaftWLkcqtuO+4fP8e5aBoofOTIWXb+Lzuma7y/dT22dmwduHMqgnp13XKer8GxfjGqrwPiTB5FO6eqRZA36wVfgXv8//BVH0CSdnJLq2fkNyDK6wZc1uNahIgvJcRFNfhuXI+Oo7HEl/Y99ReG+r1nqGY7L48dk1DJ6cMMZSKqq4ivYhjZjMA7FQ63bQkb9LCVJa0Db80K8+74HVIzjbkMyRDZ7f5IkYxw/E5ffi+/oJgAiPB6yFQ0LnT/Hj4ZR2amMHXLmWVP9usUyY+IA3lq0h30FtQzu1fj9HW+IxeKx4lf8jZZFCYdKe6B1kxIdh+K0oElqutUUTiEFjkOHDnH33XfjdrtJTU2lpKQEg8HAa6+9Rp8+oc+YOO/pTUQpEi7JiwpUW12knMOSC63NV3oA15IXUd12dAPG4j26GX/xnnMKHHVOL1VWF7Ik8cqCHTxyywgyk89+BpvQ+hRrOc5l/4b6fSMUSznaniPQdstudK5u4Djcmxfg2bGEiAl3AYEvG959q9H2GYVsOrl/haqqHCq2Mvg0Xxb6X3k97u+sXLZvNVdNGMq/Nkbz6Yp9DHFvRirZjXH8bchRCShVBaj2GrQ5wzhanzGeHnUyY1zXdzS+A2uRk3qjHXDmFSskjZaIKwITaMprnbz75sfcGf0tz0+LRdtzeIsGiXMGJvHfxXvZW1DTZOCIM8aiqApWj404Y2xgw6mdcxmTMYrBCQOave5nBxdhNsRwaWbLVuCoqt9/I90c3y4LHEKIS448/vjj3HjjjaxcuZIPP/yQ7777jptuuilsmeSdlSRJRGuMeCUVZH+HWl7dX1OMc8kLYIzEdO1jGMffhjZtAL7i0+8l4FN8pz1+rDTwx/fLqwZi1GuZ/dHWYDedoipnXPJaCD/P1kUoNceRY9ORY9PR9RuNYUzTE0wkfQS6gePxHcrHX1WI4qjFs3MZ+NzoL2iYt1VpcWG1e+ib0fzAtyRJGMb+Ak3mBXjWvMsdPQ7xW83HKJs+xl+8F+fXz6G67YFuKkCTOYQieyCHLOOUvAhNxiB0QyZhvOR2pBaubLv9YCV7vemo+ii8B9ed+Qk/YtRr6ZkWzd5jTS9mGGcMbKBU5QocP2w5xrbKXXyw95NmN3kqs5fzTcFKvj66DH8z04qbU+u2oCoSSUYD+NxtnsMBIQaOvXv38qtf/apB99QvfvEL9u5t+w1MOrpofaAJLWndVHSQmVWKvQbn188hyRpMVz2IJrEHAJr0LFRbBYqt6TWFCm1FPPL9E3y4bwGq2vRU5mNlgcAxrF8iD9wwFLfXzztfB1YrnX/gS57Y8CwWt7XJ5wrhp7hseA+sQdfvYiKuuIeIK+7BOH7maXeL02dfDig4Pv0z9rn349m8AE16VvB9c8KhosA33z4Zp//gkmQtEZf/FjmhB5EHFqMzGHmt7nKcF9+DYinDufQlfEd/QE7qhWwyU1xXQqTWhFkfc8o1NBhH3YQm7szdSz+27VAVSfFR6PuOxHdsS3BxxpYY2D2OIyU2nO7GX6QSTiQB1g+Q76raiyzJ1LotLDn6bZPXW1G/X7rd62BP9f4W1cXuq0P2G5HcdUDbZ41DiIEjOTmZjRs3Nnhs06ZNJCeHnhHaVQSyx0Fj8LbpAHngA2It3v1rGv1zLp6N6qojYtLvkWNOrudzYkVNf3HjZanL7VX8e9tb+BQf3xWtY3EzfwBHS20kmo1ERejolhzFxReks/+4BY/Px8ayHyh3VPLqtrdw+c78WqiK74wtIFXx4zu+C/UMLSEhwLt7Bfi96C4IfZUHOTqJiKsexHDxL4L/jJfc3ui8Q0VWDDoNGUkNxxssbhsFtuMNHpN0Rkw/eZCIK+8j+oYnOCp1573tEobxM/GX7EOpPIq2+zAUReVQdRFpUaGvF3U6Lo+PfQU1DO2TgK7faPB78R1t+QZ0A7vHoagqB45bGh2LNTQMHDuq9tAvtje5KReyvGAV5Y6GX8zqvHbWl2wiN+VCIrUm8su2tKguTsWOVolAqe+ykiLOPNW5tYU0xvHAAw/wm9/8hksuuYT09HSKiopYtWoVzzzzTLjr1+mYI+LAUUxMjL9NN3Ty/PAF3p1Lmz4oa4mYeC+apJ4NH47PQDJE4SvZi27A2ODjdV47L656Da/i4/9yfsfSYyv58sgSYo1mRqflNLhGQamNHqkn58H3yYjhm02FbDy2D7vXwUVpeawv3cQbO+dy95BfnXbw0Lv3O9zfv0vElEfRpvZrdNxXuB33+nkoNcUYRv4M/dCrQnhlui7V78W7azmazAta/E1d220wMLjZ40UVdazdVUq/TDOaH+V8LDy0iPyyLfxmyG1kJfQPPi4Zo9D2HI4ZmDa2Fx8sO8DSzN5cOfJnuDctQNNzBHOX7aNEKqWXb1CL6tuc3Udr8PlVhvZNRE6ORYpOwntwPYxp2XunbzczGlliX0ENQ/o0nD1o1BqI1JqodtdS6aym1F7GmPQ8RiQPZXvlTj498AV3D/1V8Pw1RRvwKl6u7HEJBo2OjaU/4PK5MZ4yW+10PDiIkMyojkCg6rBjHJdddhnz58+nX79+2O12+vfvz/z587n88svDXb9OJzoy8I0+LtLVZIuj2uri45UHOV5e16rlKlXHkBN7EnnT043+Rd36AtrMIY2eI0kymvSB+Iv3Brui/Iqf17f/lwp7FXcN+SXpUancmnU9A+P68cHeT9hfczD4fIfLS3mtk56nBI6+9d0Wm0t2Iksy0/pezc8HXMee6v28s/t/2L2OZu/Bdyzwzct3YG2Dx1WvC8fXz+P8+nlUvw8pJgXvwbVNXUI4he/QBlSnBf0FE898cgvU2Nw8/9E29DqZGRMbD/4esxaiqAr/2fluo5bHCRNGdGPU4BTmf3eYfHkYUb/4fyzZ72PV7gNIGj+HDwfKaanV24rZfujkN/xtByuJMGjo1y2wtaqu7yj8Rbvw1bVs8yWDTkPv9Bj2FjT9vHhjLNWuGnbWbyqVnZCF2RDDVT0vZ2fVHlYXrQ/MHFN8rDq+hoFx/UiPSiU39UI8ipftlbtCrotfdhKpiQzsNU7br1MFIQYOgF69evGb3/yGxx57jNtuu42MjIxw1qvTiokODOiZjA4qTgkcLo+PBd8dZtac9Xy9voDnPtxKZSu1SFRVxV99HE1iT+SY5Eb/JGPzs5w06QNR66pQbRUAbK3YyWHLUX6dczN9YwPTMbWyltsvmE60LpJVx08OLh4rCwS/Hqdk3sbHGImLNlDgPEwfc09MugguSs9lau+r2FK+g8fXPc2Kwu8bDQgGsob3ABLewxtR/Se7orx7VuEv3I4+70Yib3gSffblKFWF+KuLzvm1O1+pqopnxxLkuG5oMppvObSUw+Vj9kdbcbp9PHDDUBLNDTOq3X4PZY4KxqTnYdKaeHXb203uyS1LErf9JIusHnG88/Ve/rfyKJ+uPkD84D1oJS1+azwfrzzY6Hmn4/MrvL9sPy9/uoPdR6tRVJXth6sY3CshmH+h7TsaVBX77jUtvveB3eM4WmrD4WrcTRpXvy/Hzso9JJsSSTYFkiEvzbyY3uYezNs3n+c2/5uvjnyDxWNjQvfAFhG9zT2IM8SG3F3l9HhA6yVaX581LklIxrZfXy2kwPGvf/2L7du3A7By5Ury8vLIzc3l22+b7vfuyvTRSRj9ChqtE6vdg8vjY/X2Yv44Zz1frD3KsH6J/P7GoXh9CrM/3kad03vOZaqOWnDbkeO7tfi5mvQsAHz14xzfFq4mKSKBcT1HNjgvQmtkYHx/DtQeCs6UOjGjqntqwzduZoYGl6aG7MSs4GNGywC6115Ft+h0PjnwOc9u/n8NZlz5iwJZw7rsK8Btx388sFWxqvjx7FyKJrU/hmE/QdJo0fbOA0nGdxYzZLoC1esOJL9VFaK74IpWzbl6a9EeSqoc/PbaCxot1QFw3FaMisoFiYP47bCZeBUf/97+dpMzh7Qamd9Ou4C0hEiWbSogPnsXTk0Vv8q+mUnDsli/q4z9haFnZB8pseLxBnKnXlmwgzU7SrDUeRh6SteSJi4dOaEHth3ftfjeB3aPRVVh//HGdYozxlLprOJA7WGyE06+77WylgcuvJtbBt5AlauapcdWkGpKZlB8/VYSkkxu6nD2Vh/A5jlzL0SDrHGnBckY0+TyMOEWUolffPEF/foF+pxfeeUVnnnmGV599VVmz54d1sp1RnJUIAlQlQOticfeyuftRXtJjDEya/oI7pqaTXbvBO69fggVtU5e+nQ7Hm/LpuP9mFJVGCg7IbPl9Y1NR4qIwV+8l8OWYxy1FnBp5ljkJqY89o/rg93roMQeWFjtWJmN+BgDMT9K/jImBt7cPSICOT6qqvL1+mPs3a+Q5ZvEdf2uocBWxDFrYfA5voKtoDNiyL0OyXBy2qTv6A+odVXohpzsbpFNZjQZg/AeWt/sbK+uSFUVvPvXYP/oETxbv0TbZ+Q5J3eeqsbmZsv+Cq4a1aPZ3I1CW6AVmBmdQVpkCjcPvI5SexnbmuiKqXJWY/VXceuUNPqNOo7TWMwN/acyLCmbq0f3ID7GwNyl+/EroU3p3nusBgl45JYLMeq1vL1oLxJwwY/GJHQDx+IpPYS/rGUtmj4ZZrQaqclpufHGWDyKF5/i44JTvjBBIDhclJ7LX0c9xLS+V3NL1vUNgnlOyjAUVWl2T5FTncgaT4w0o7RTDgeEGDicTicRERHU1NRQWFjIxIkTueiiiygqEl0FjehNRCngkQLdVH5F4c4pg5k1fUSw/x+gf2Ysv75mMIeOW/hoRcvewD/mrw70I2vOosUhSRKatIH4i/fwbcF3RGgjGJna9LpC/eMCgWB/zSEgMKOqRxPfOut0x1FcJqzV+uB5ZTVOTAYtn31/lMExQ5AlmZ2VgVZOMGu4WzaSzoC2Tx6+o4Fpk54dS5Cik9B2H96gDF3f0ai2yhb/8Z/PvHtW4lr5HyRTLBFTHiXisrtbdfG7DbvLUIExFzS/70SB7TjR+qjgVNphSdkkGuP5tmB1g/NWFq7hL+v+yRMbnuPFHS9zXNnNFd0vYXy3QKAz6DTcNKEfxyvq+Gz1kZDqt7eglm7JUXRPieaBG4YSYdDQt5u50RcbXf+LkY2ReLaHti3ECXqdhj7pZvYVNG5xxNfnchg1RvqYm94Eyqg1cnn38fQ292zweEZUGumRqWwuO3PgKK8fm0mOjEN1Wjt24OjZsyeff/4577//PmPGBBY4q66uxmhsu60SOwtJkoiStDjwMmv6CP7x61GMHJTSZHdB7sBkJlzYjRVbiigoO/slmZXqQqTI+NMuw3A6mvQsqjxWtlbs5OL0kc3O7og3xpFojGd/zSGcbh9l1Y4GM6oAPH4Px53HUC1JHK5fx2j9rjK0GokHbhyKx+tn0Zpiept7sKN+IFGpKkB11KKt31FO23cU+D0ULn0Ppewg+guubNQc1/a8EDR60V11Cu++1cgJ3TH99M9Nzko7V+t3ldIrLYaUuOZXQyi0FZEZfXItKlmSuTRzLEesxzhiOQaAxW3li8OL6R/bh9sG38xtg2/mnqG3M6XPpAbXGjEgifHD0vlq3TFWbDn9l1SvT+FgkYWB3QMf4N2So3hi5kju/mnj7HhJZyR6+BX4jm5GqR/bC9XAHnEUlNmwuxp2McfVT8nNSuh/VsuODE3K5ojlGHUe+2nPq6qfSRXMGm+HgXEIMXD89a9/5YMPPmDDhg3BrWK///77YBARGorWGKnDR98MM3rd6d9EPx3Xi0ijjve/2X/W3S5K9fGzGt84QdttMGvNJiQIfuNrTr+4PhyoPcyxskBQ6PmjwLGv5iBexUeS3INDRRYURWXjnjKG9EmkT4aZK3IyWb29hHRdb4rqSqhx1Qa6qZDQ1M/80qT0xa2PJa54LU5Vz3p3n0bdFZI+Am3P4fgO54ucDgJ7aygVR9D1G93izOpQFFXaKSivY9Tg5jc28vi9lDrK6R7VcOLMqLQcIrRGvi0MtDoWHvoan+Lj5wOvZUTKMEakDCMroX+j7lFJkrj1yv4M7ZPA3KX72LK/+Q/5w8UWvD6FgT1OLokSH2MkNqrpL0HmnJ8AciArvgWye8ejAit/FMhSTElEaCPISxne9BPPdN3Egaio7K7ed9rzLG4rqgopMbGoTku7JP9BiIFjyJAhzJs3j/feey+4B8eUKVNEHkczonUmHJIa0lICkUYd143vzYHjFtbvLmtxWariQ6ktPqtuqhM8phjyYyMZ4vARqzs5A0tVFFzr5+GvONlV0D+uD06fk+3FR4GGM6pcPjdrijdg0OgZkNCHo6U2dh6pxmL3MGpQ4APnmjE9MUfq2b090IWys2ovvoJtyMm9gn8Eqiqxyd0TgD26wbzzzVEeezufnUeqGtRb13c0qsuGd9fyBkFXcdTiWj8PX9Hus35NOhvvwfWAhLbPqLBcf8PuUiQJ8k6zDWxRXQmKqpAZ0/C9aNQaGJM+kq0VO9lUtpUNpZuZ0H0cyaakZq50kkaWuWtqNj1TY3j981289Ml2XvpkO6/M38GRkpMrEuypH98YkBnb/MVOoY1JQNsnF+/eVS3KJO+TbmZY30S+XHuswXRhky6CZ8Y+xpCkwSiqyqerDnGwqHGyYHO6R3cjWhcV7L5tjs1bh+QzovW5QPF3vBZHfn5+8Od169Y1+09oLKa+f9ca4lIbY4ek0zM1mo9WHGxySYPTUWpLA6vcnkPg2FKxE7ekMrragu/QhuDj3r0r8W5fHNhjut6JcY4DNYeIjdJjjjKgqApri/N5fP3T7Kjcw4TMsfTPiMfjU/h01SEiDBqG9g0MUEYYtEwb15vCQpUYrZmdZdtRyo8Eu6kgsEz7N5be1MUP5OIbp/Obn2bj9vh5/sNtvPDxNoorA815Tbds5JS+uNf9D8dnT+Ar2o37h8+xz3sY7/bFeDZ/dtavSWeiqireg+vQpA887VIi53L99bvKGNQzHnMz3+DhlIHxqMZT9S/pFuideGfX/4g1mJnYY0LI5Rv0Gu67fggDe8RRbXVRbXWxt6CG1z/fhdcXaInuK6ile0o0JmPoYzr6CyaC14V3b8tmWN10eT/8itpobPJE99y2g5V8te4Ysz/aFnK+lizJDE4cyO7q/af9whnMGj+Rw9FOLY5mM8cff/xxvvzySwAeffTRJs+RJInly5eHp2adWHREHLiPYrWV28YvFQAAIABJREFUEhdx5j9kWZa45cr+/OPdzXy94RjTRmfg/Ob/oUnojmHkjad9rlI/MC7Ht3xG1QmbSreQGJFAzwgNnh1L0Pa7CL/Dhjv/U9Aa8BfvwV12hOe/qeZYmQ05y0Sh8yhZKf3x+L28uu0t9tceoldMD+64YAa9zD2CyY+F5XVcfEEaOu3JLrvRg1OZ/91hJFsq+3wH8Egqpu7DgseXbixAjkok+dr/QyPL5AyEoX0TWba5kC/XHuUvb27koZ8PY0D3OExTZuE7sBb3xk9wfvU0ANqeI0Bvwnfge1RX3WnzWM4HSsURVEsZ2qE/Ccv1DxVZqbS4mHpx04O+JxTajhOpMxFvbPytP84Yy/CkC9hcvo1pfa8OOUv6hJhIPfffcPLLxc4jVTz/4TaW5hdwRU4mh4otXDaiZV+eNEm90KT25/+zd96BVVRp///M7em995AE0gghofdQpRdFRRF7xfbu6uuyRX+r7ur6qmtbu6tiF6UjhipVCC0hgVQC6b23W+f3x4ULISEkkJAQ5vNfZubMnDO5M8+c8zzP99GlbUUZPa3TYcvujlbcNMKf9ftOM3GINwP9Wz/jiQfzcbJTYxJF3vwxmT8vjcPJTs2x7ApW7zrFQH8nlkwJbXO9KJdwfi8+xKnaM4Q6tS+VrqURjWBrzuGgd+RGoAPDcc5oAFK+Rhext3GFGqivLwH38Ms3wDwFjgtz47cj+UxrWINYkIqxIBVFUDxy90vr7Zuq8kGQI3P0uuQxHVGrrSOjOpsZgQmonDRod/0XY3E61UXHQNeM1Zw/0bzp/zizcw1ZBTFMjPUhT+lPmTqHOQO9+eLEt2TW5LBk4CJGew+3PAzO9mocbVXUNOjarIsrFTIShvqwLqUQ9UATpxydiHcxL4HmFteRWVDLbQkhraQslAoZN40IYEy0F3/68Hf2ppYw0N8JQZChDBuLImgY+qx9yJy8UXgNxFiWgyFzN4aC4yhDRl3RvekNRF0zyGStiiW1OcagQ2xpQGZrDonVZ+8HmQJlUPwl21xIZXM1ThqHdkOu22P/iRKUChlDwzpeWsqvL8TPtv0iTQDzQ2YS4hhMnHtMu/u7QlSQC7Gh5iUjJzs1BqNocYx3BWXYWFp2fYapKh+5S+dLYc8cFcC+1GK+3pLJ8/cMs/xWz5TUk5Ffw+JJIUQEOvHK10d484dk7G1UnDxTjZ21km2HC7CzUjL3IkM8yDkUuSAnpfwE3lZ+2LQzezLKmrEWPM5njfdlHweA0Wjk8OHD/PLLLxw5cgSj8epyD/oz9nbmF2VtY/uqs5di2jBfZsn2IBYcRz3yVgQrB1r2rkTsQJrcWFWAzNGrVUGernC49BgiIvEesShDRiFo7NDu/4a6I4koIyej8AzFEDQKt5rjjAux4q7pA5kWEIhR0LP+5FscK09lUchsxviMaPXCEASBgf5OONur232gJ8b6oGx0QmUSyXR1t7TdkpSPRiVnXEz72kr21iqigpxJyanEdIFfQ1CqUUVMMpcsBWRuQQgaO4tc9/WAaNDR+PMLNK1/BbGD3IWWHR/R+O0faNn1OaamGgw5B1D4x3Qqqi6vroC/7f8n/zr0Dllnw6ovx7GsCmIGuGClvvRvTG8yUNRYip/dpRUlnDVOjPcd1W0JibdNDsUkiny5OQOZIBDWSf/Ghcj9zQEZhjPHutROrZRza0IoBeWNrNp5/j4mJuWhVskZH+ONv4cdjy2MpqSqifyyBu6YGsbrj41hTJQna/bksju5qNU5rRQagu0D2XnqKK98faRNsIzOoEeU67BT2llmHL1RNhY6KXKYnp7OY489ZinkVFJSglqt5t133yU8vHNf1DcSDg7mh6e+qWt6OP5lu/DUZLNHHMr06BkIGntadn6MIWMPykHj221jqsxH7hnW7r7OkFR6DF9bH9LSdRxsLiTQNpagil2gsbOU51xXOYD57GSBZx6mhoH4HdwAHgoy5Tom2Ida5BMu5s5pYej0JmSyti8Ke2sVsweYON2kI82hBVEUKalqIim9jMlxvsgVJvYWHWCkZ3yb8MaYEBeS0ss4U1JPkFf7U3VBkCH3H4zhzDFEkxGhnRDJMyVmmexBAd3vF7gSdCm/INaZcyX06TtRRbT1AxgK0jDkHkLmEYI+Yzf6zD1gMphDmDtBcrlZQ6xe18C/j35IjFsUy0cvBdp/mTdrDVTXa9uEXV9McUMJRtGIv/2V+9q6itvZJaN1e08T5GXfoWG7FDJrR2RuQRjyU1APndultnED3UgY6sOvB/NxttMQP8idgyfLmDTUB2uNuS+Rgc68dP8I7KxVlm3LbhpETaOOLzZnoFErGHY24EBvMFKeZ4fJIYeiunJKqprwcjn/MVBcW4MggKPGAbG5DmQKUPVOobhOzThWrFjBHXfcwe7du1m1ahW7d+/mzjvvvKTv40ZHbeWEwiRSr+t8HQrRoEN3bD11LlH8WB1JSnYlitDRZufvwR8RtW3ju0VtI2JjFTKXK3tYS5vKyasvwFTpxddbMlmzJ5dPszyoMVnzVeUQvt1dSFJ6Gb9l66m0H4Q8ZxfNm9/AtqWJUBsfhmplzMhMRzS0L0Zno1HiZHfpJZcRDmWEN+ioNTbznx2JPP/ZQZQKGVPifNmQm8g36T+1m3EcHeyCgNkJ2REK/xizfElZ2y9rkyjy/tpU3lt93OJg7U1M9RXojm40L016h6NN+gmxpbVjVTQZ0O77GsHODetZz2Jzy0so/KKROXq3Ci7oiOOVJwl2COD5kc8wO2g6JyrT+ceudy8pe19abRal9LxMJcuOHOM9ycyRAfi72zI8/MpLPCj8YzCV5mBq7lrdGEEQWDIljKFhbny3LYsP16VhMolMiW/tb/RwtrYYDTBLrTw6Pwp/D1veX5PKG98fI7+sgY83nKTktNlAyxzLSc5uHUVYVGv+28XaAVNzHYKVfa+V8O6U4Th9+jTLli2zdFIQBO666y5Onz7dk327bpHJZNiJAvX6jpN5LsRYlgNGA65DE3C215CYlIcgCGjGLEVsaUB78Me2barND+uVhuImlZiF1XJO2LJgXBCf/O8k3nx2JvZ3volz7ES2HS7g/TWpuDlq8Bk3D7SNmKqLsZq6nCeHP8GyIfcgNFSiO7bxiq6vKU/DvcUNU6M9qdq9xIWbv84Mynp25O8BIK2idX0OnVHH+rx1+AbqSc6pbO+0APy4M5u0Fk8Q5BjbWYZIzq6grLqZxhZDmzDftNwqS16NoSSTlt8+bbN0VNZUwWepX9N0kdpvU4uBj9alsT+tpNVS2uXQ/v4dAOpRt6MefQfoms3BCRegT92KqaYIzeg7EBQqZI5eWE1/EpvF/0BQtK35fTHVLTUUNhQT5RKOSq7ipqDJ3B+1lDM1hXyS+lW70TylVeZQ1cuVQM6rL8BKocHV6trWnFcp5bxw73CmD++8f+JiFP5DABFj/vEut5XJBB6cE8EAHwcy82sYGuaGu6PVZdtZqRWsWBrHbZNDOVVUx/OfHeRQehm3jI4x54R4FnEk90yrNqXnssZtz2aN95JjHDppOCZMmNDGQb5jxw4mTpzYE33qF9iioN7Y+UJOxqKTIAiofAYxOc6X9LwazpTUI3cNQDl4OvqTO9Edb11vw6JRdQURVaIosisvCWOtCxMig5k9OhCZICATBBxs1Sy/ZQj/757hjIr04L5ZEah8BqEaMgvN1EfN0iCCgMJrIIqQkeiSN2GqK+vS9U01JYi1pbiFjyBMGINM3YJXeDFOdmp+zFyLWq5ikFMoaZXprcQQj5WnsrfoILXue8irKaamoe1sJ7e4jl9+z+On/cXIvcLa9XNsScrH2V6NrZWS39PO58+Iosh327LYdriAvKJqWnZ+ij5jN8aiE62O+T5jNYfLkkkqbW2UfjtWyO8nSvl4/Qle/vIwWe0I4l2MofAEhtxDqGJnI7N1Qe7sizJyMvqTOzGWnUI0GjA1VKE9vAa532AUAUMue872SK00G+ELxSejXMN5IO52TlZl8k3GT23W1UurmhDgsi/DM3X5+Nv59toX8NUgc/VHsHK4Yn+YSinniZsHM26wFwsnXDqQ5WIUchnThvnxysOjmDHcn4Xjg5k+3I/pAQmI6gYKXDayOvMXmvTNGEwGKhrNhsPL3skscNiLhqNTi4JGo5Gnn36aqKgoi48jNTWVyZMn8+yzz1qO+9e//tVjHb3esJWrqdF3LobbJJrYX5bMliB3xhXtZ0LMeNbtOc3Ww/ncNysC9fDFiHXlaPd/i2DjhDJ4GMbSbHN1N5U1gk3rr7xDJUfZkJvIiuH/g+oSWkW7stNoNNXiIx/JndPC2n3gfd1teWDOeUlu9fBb2hyjHnErhtNHafntM6xm/rHTTvpzD6lb1HCetnPjixOlbMv7DSuFhvTqLG4OnYuN0povTnxHXn0BgfbmL8qk0qM4qOzQG02oBh7mQFY402Nby2tsSTIb1MLyRhrCB2GVuhpTfTkyO3NU0JmSetLzzJEv5bXN7EkptuTPpJ2uovBsnkjZ/nU415WCTI4++3cUvmb5imPlqaRXZ6EQ5BwqPWrJtjcYTWw9XMAgf0fGRHvx0285/POrIzw0N5IREe1nXIstDbTs/gLBzg3V4POSG+q4+Riyf6dpzd/PHyxToBm9pFP3tz1SK07ionHG07r1ss7kAWPJqyjll9NbadQ3sjBktiU5r6S6CWd7TYcKCFqjjsLGEqYFTLrivvUmgiBD4R+D/qwKgSDruq/E1krJPTOvzN9ra6VkcUKI5e8RXnEoWlz56NBPbJXvYGvBDss+URTwdnBG31yPzPnKZ1lXS6fuUFhYGGFh5x2wISEhjB07tsc61R+wV1pTYKhDNJna6CyZakoQ7N0RZDKya3L5MWM1BZpmFAjsKtjHtIBJDAt351B6GUunGVEp5WgSHqJ542u07PgQQ9Y+c9EjKwd08UvbvPQPl6VQ3lzJ6bozhDmFcDEmk8ia9O2gUfD4lOltKrh1BZmNE5qxd9Gy82NafvsUzaQHLf0xNdWCQdeqXO05DHnHkDn5WF7m8wfMJKU8jTU5m/C28WS8zyiajS0ICKRWnCTQ3p96XQPpVVlM9hvPELcoXkt6n1/Lf2KC4Sk0CrNuWlVdC0npZYyK9ODAiTIONXgyDtAd33K2qh3sOqI9G/niRVFFEzuOFHIksxx/XycSk/JxsFExyE0kuHIP8sChCGprDLmHEMfehV6An7LW423jyVD3GDbk/kpFcxWuVs4cyiijul7L0ukDGRLiSvxAd15eeYgN+04zPNy9zf9JNOho+vXfiI2VWM16ttVyk6C2wWr2s62ifeQeocgcLi0w2BE6o56M6uxWIdMXMitoKmq5il9Ob+XFA68zwXc0s4OmUVrVhIfz5WcbJtFEkH3vvciuFnlADPqMXRhLslB4937AT2yAP+p1wwi2NjEo0khji54tSfkEO3ujUijQNdchs7r2dTjO0SnDsXz58p7uR7/DTmVHo64MY1MNCtvzMwJ91j5adnyEKnYOzYOn8M7Rj7CXa7i9pBZ1zGw+L91NZnUOoyI82JNSzLHsCoaHeyAoVFhNf5KmdS9jKEhDNXQun5zyI31rM/8XZkStMn8RmkQTWTWnALOKbXuGY+ORk2itC4m2jcfR+sqEES9EGTYGU0MlukM/o7N1RhU7F13yJnQpv4Agw3rOn5C7BliOF3VNGIszUV0gle6gtmdW8DR+ztrALWHzkMvk2MpsCHYIILXiJLODp3O4LBmTaGKYZyw+tl4MNE0mXZ7IptxtLAydBcC2IwWYRJEF44JpbDGwPbue8a7e6FMTLaV1E0xW2A66B2uNkgE+9rg6aPg9rYShEZ6knqpiwbggYktXAyIFfjcRbK/DkLkHQ94xEqmmWlvDsoiHcdY4sSH3Vw6VHmV6QAKJB/PxcLa2lBZVq+RMG+bPZ5tOkna6iqig8/LeoslEy/YPMJXmoJnyKIp2IuPkzn7IryKx80Iyq7PRm/REuQxqd78gCEwNmMgIrzg2nEpkZ/5eDCYjJVUuHepTAZyuzQMg0OH6NRwKn0iQKcwqzX3AcMhkAtHBzhzPruKJm8bz3urjCGUaHpg/EnRNYDL0mtwIdCGPY+/evaxYsYKHH34YgOPHj0uSIx1gr3HCJAg0VJyP6DEUnqDlt09BkKE7sZ3f8nZjFE08ohpATKOewcET0MjVJJUeZaC/E462qlbr74LGFuv5f8PmjtepCprO4VP1NLYY2JtabDmmoKGIZkMzAgKZ1afa9Ku+ScfmnF0IwK2Du6/0ryp2DspBE9Ed20jjN39Ad2QtCr/BCCprmn95o5UKqT7nIIhG5Bet1Sf4jeOfY/9qkTUBczZtfkMRNdpaDpUcxdvGEx9bc7LjhOAhGKs82V3wOy0GLS06A78dLSIuzA1XRytGRHhQVaejeOgjWM//G9bz/8Yhn9uRYySh1hy1JAgCIyM9OHGmmi83nUSpkDHRqwGHihR+00ez7Uw165pz2eDpwk+nNrMl7zfiPYYQ6hSMi5UTAxwCSSo5SmZ+DadL6pkW74vsgi/6EREe2NuoSEzKbzVW7e/fYjh9BPWo21EGD+u2/8OlSK1MRyVXEerY8Rq8vcqOJYMWEecRw6HSYzTrdHh2oIYLcKruDO7Wrtgqr/4jpLcQlBrk3oPM4dsd5E1dS2JCXGlo1rNuby5HsyqYMyYQJzu1ORQXEKz7uHN85cqVvPDCCwQGBlo0rDQaDW+99Va3dGLt2rXMmTOHiIgIvvrqq3avP2PGDObMmcO8efO65Zo9jb2TOdKpYufHtOz7GkPRSZoT30bmYI6E0eka2VOwn8FukTiUnkbmFoTayp4YtyiOlaViFA2MiPDg+KnKVlUCBZUVMo0dW5LyUchl+LjZsCUp3xLBc65WRrxHLKfr8tAZda369eOuDESnPCIcI3DuhBxKZxEEAfXYpSgGjETm5I313D9jNXU5VjP/gGjU07zpdYxlOTQnvo129+fInLyRuw9ocx47VWt5kHOO3N8K9pFbl8cwz/Pqo+EBTtg1hqETtby+ZT0b95+hSWtg2jDzl29sqCsqpYz92Y3I3YM5XmfPV6lK9rkuQtZUaV4mMugYGeGJKMKBtBJGR3kiP7EJwcaJ2oBJpNYnsT1/N0m2Sg5Sj6vaiQUhsyx9GOYZS0lTGRuOpmCjUTA6qnUG/7ks+dRT530nhqJ09KlbUEZNRRU9rXv+AR0giiKpFScZ5BSKspP1OeI9htBsaEbmUNFhRJUoiuTWniHIPuCSx1wvKENGIdaWoDu4qre7AkBUkDMyQWDd3tN4OFkx9WyY77mwYUHTx53jX3zxBZ9//jm+vr58/PHHAAQHB5Ob27kCK5cjPDycN998k48++qjNvsTERDZv3syqVauwtbWloqJr2di9hcPZ7PEv/d1Q1h5GcewQM21tiL7pfxBsnDjq7UeTqCXBczim339DFXMTYH4RHSg5TGplOiMjAvn1YD6HMsqYOOR8fHxDs559qSWMivQgMsiZD9amkZJdyZBQV7KqT+Fu7cowz1iSSo+wPSOVgweNGIzmr6gSWRrKAAOzQrvfkSnI5FhNfrjVNrmTD1bTn6R542s0rXkRlBpUwxahip7eblLexXjZeOCscWJr3m+A+YV2DpVSzktLZvD3PRkUiqnk7Hci2NuBAT7mB0qjUjA01I0DtTuwPl7I1kQ5AR52zJwbi7zAmZat/6Hlt0/xnvwIbsHl1BjLmB7ijXH7SVTDFxNv78PBjFICNQP4Y+g0mla/gHrcLFTq80sEQ9yi+CFjDVbaTcyIutWyZHghE2N92LDvDFuS8lk2PRTtvq8QbF1QD7/5iu7zhZysymRN9iZL5JmLlRPLIm7HSnG+Vk5+QyHV2hpuCprc6fNGOA9EJWgwuBTheYGP46es9bhonJnoZxYtrGiuokHfSJDD9W84FKGjUZadQpds/nBQRU3t1f5Ya5SE+jqQkV/D7VPCUCrM3/nn5Ub6+IyjsbERLy/zl9Q5x5rBYECp7J7qYmFhYYSEhCBrx0n72WefsXz5cmxtzV+irq6u3XLNnibA3p8RnnH4ug3E3T2MJo01X7pZUYwWEZE9dkp8W/T4pJ9dtvE2rz0PdArBXmVHUulR/D1s8XKxbrVcZRJN7DiSj85gYtowP+IGuuFsryYxKQ+jyUh2TS6hjgMY4BCAgMC6Y0k0tRhwd7LGzUmDrX8hgXYBliila4HCayBW0x5HGT0dm1tfQR07p1N5B2D+vUW5DMIkmghxDLJUWjuHRqVgUeQUBE0TccNEbr9IPM4rqAHcctletAN7GwVP3hKDRqVAGTwMVfwCDDkHKD/1Oy1uKSg88tHlbQOFGlX4BBxddcjULeirXJG5BiBz8MSQtQ9Tcx2m5joMRSeRbXyDgY0t5DsaGK7MbHcM9tYqRkd5sj+thPpjWzBVFaAedXuHelSd5UhpMmVN5bhZu+Jq5cLxipP8cvp8jQlRFFmb/QtWCiti3NoWNboUcpkcVzEYuWMZNjbm+5lWmc72/N2sP7WZZoM5vyO3zpxrENwPDIcgCKhH34EicCjafd+gP5V0+UY9zNyxQSyaEGzxmwHnl6r6uo9j2LBhbWYDX375JSNGjOiRTl1ITk4OycnJ3HbbbSxcuJAffvjh8o36AGq5irsibuXB6Lt4KO5h/mf0M2iUVrx37FP2Fh2g3NDAuGYZhpPbQSZH7mEOKZUJMuI8YkirOEmzoZmRER5k5tdQWWuW5fgw5Qt+rf6eiCAnfNxskctkTInzIz2vhqQz2bQYWwhzGkBdgwhNjsjtq3nuzqEsXxjNyNEmtEI9UwLalwjpSRT+MWhG3Y7Muut6QlGuEYB5+a09hrhF4aR2RO+UzQDv8w+T3mTgUMNOMMkRlDrmzbDHwea8wVLF3ITg4MHP6T8jCCICAoerTqIcOA5BbcPJs0V18rKtqK7XoggdhbEkk8aVT9C48gmaN7yK2FSLlSGcOoWcovLDlyzGNXWYHypjE8aja5D7RJoVfLuBwsYSAu39zb+zwcsY5TWMHfl7KGk059Uknw0dnh00rcs+CFW9H4LcRGrlCfQmA6sy12GnsqXFqGVfkfmlmlubh1quwsumYwf69YIgk6FJeBiZxwBzBGNJ+x8D14rwACdmjQpstU1srgNBQFD3nupzp5aq/vKXv/Dwww/z448/0tjYyPTp07GxseHDDz/s1EUWLFhAUVFRu/v27duHXH7pJQuj0UhxcTHffPMN1dXV3H777QQFBTFsWNccii4uV36T3dyuPuzNDTv+Yvc4f93+f3yXsRoXKyfG+cVTt+Nr1N6huHufn0lNk49lR/4espuzmDkultW7c0k8XICdVxWplSfBGkZGqy39WjA5jHV7c/n15FHQgNjoytvrU8DOBdxP4edrHvu6/b8Q5OTHlPCR7c7uemLc3cEE1zhs7ZTEekVdsizn7EGTWZn8E7WySkJcAgFYfWIzFc2V3B29jO8zvqfAlIWbW+uqlYfHzCQlcyNz7ALIbKrgmK2eu8fPR+VsR3pKJn52vpw2WLFm72meuXURDe4eiGcFPmUqDdaDRpL6j60o7E9zVN7IWF0xGt+BlvO35KdjqC0nBLjX9TCCUYfX7AdRuV79MoNJNFHSWMrk4DGW/9W9djeTvCmVdWc28scxD7P69434O/iwcMjUDkuatve/bqy2Q2ljQ0r1cQxKLWXNFawYv5zVJzezu2gft8TOIL8pn1CXIDzce+/r90rp6PdtXPIXir5YgTbxbVyWvYzK1eyzNOm1aIuy0PhH9lqyY7nYjNHaHnePK7vn3fFcd8pwuLu789NPP3H8+HEKCwvx8vJi8ODBnXr5AKxevfqKO+jt7c3s2bORyWS4uLgwevRoUlJSumw4KisbMJm6XprVzc2O8vIrrwd+IVbY82DUMv6T/CkJvuMxuQ9BUK9F9I5qdQ070QlPGw++S1nPM/EBDPRzZEtSLurBe8Boi1yt43jNAcaWR1jajI/xZmftAQTRhvd/yESlkHHzuGGsLszmQE4qWTWnqGqu4Z6IO6isvLwUSneOuzsIUAVTVdl0yf0xDoP5WfELL+x4k2n+ExnqEcNPaZuIcYtimEckGVXRHMg/yoKAuZakSKPJyMrCwziJcoYfP4rKTs1PTmpSqqtxaRHIrDjFjMDJhI8MYO2eXEZFeBAe0FpMMCe7mtp6I+GqEI7bplN8cAu2arOyr6EgleZN/2c5NgRIbI5mWqMVduLV39uypgq0Rh1OcpcL/lcCMwOnsiprHX/f9hYVTVU8FftQh/euvf+1SRQpKm8i2D+UlNIUTpRnM9g1Eh+FP+M8R/PR8S/ZlLqLMzWFTPOf2Kd+K52hM79v1bSnaVr7IoVf/x3reX/BWJxh1o1rqEQz9XGUQd0za+wqzVUViOorez678lzLZMIlP7g7HY4rCAKDBw/mpptuYsiQIZ02GlfL7Nmz2b3bXKu4qamJw4cPM2hQ+7Ho1wNhTgP417gXmOg3BkFtg82S/0M1uHUBHkEQuDdyCVqjjveSP+WhhWHMmKNHpm5mWfQipgSOJa0ynbKm8yGuiyYEoXGuZbhfBP94cCSvLx/D+JBI5IKcvUUH2Za3ixGecf1iLbo9rBRWPDvscSKcB7IhN5GXDryOiMiikNkADPOIRWvUcbzivHTIzoK9FDWWsChkNkqjgajqOuSCjKTSo5yozEBEJMp1EDeN8MfVQcM3WzPb1D5PzzNLiowJGEGLXEZq8RFEkwHReFaQ0N4d68X/wGbxK1QmPM/G5lhST1V1y5iLGksALOHJ5xjvMwpvG0+yak4R5x5DqFPb6LXLUVXXgsFoItw+CpNowiSaWBRqvpfRrhG4apz5OWuDOfGvn/6mZPZuWM34H8SWBhq/+19atn+AoLYxy/Vn7eu1fpla6ntVbgS6YDh6kg0bNjB+/Hg2b97MW2+9xfjx48nONpeEO6P8AAAgAElEQVRlvPvuuykuLmbWrFnccsstzJkzhzFjxlzmjH0blfyCDGGlpk1mOZhfBg8Nvouypgo+SfucfWV7iHUfzMiASBICxiAXZOzI32s5vqCxEL1JT4znQDydrbHRKFHJVQTa+3Gs/DhKmYJ5A3qmQlxfwdXKhQeil/L00EcIcQxmfsgsXM6K7oU6BeOgsiep1CzsmFaZwZqcTUS7hjPEfzTq4bfgOmg0US7hHC5NJqXiBHZKW/ztfFEp5dw2OZTC8ka2Hylsdc30vGpc7NUM9wnHVq7mqEbEWJCGPnULpppiNKOXIHf0Ruboif+AQOytlSTndE9kYGFDMQJCG/+CXCZnyaBFDHQKaRU63BXOiRsOdPMj3mMI8wfMxNXK7KCVCTIm+Y2j0WCexVzLQItrjdwtEKtpjyNz8kYz4T6sF7yAImQkhrzkdhWrrwViU+/qVEEnl6p6mtmzZzN79ux292k0Gl577bVr3KO+QZhTCHeFL+a/J75FKVOy8OxLwF5lR7xnLL8XJzE7eBpG0UjimZ0AbRK8Qp0GkFN7mplBU3FQ9w2fRU8T4hjEk7EPttomE2TEewxhZ8FeTlZl8knqSnxsPLk74nYEQUAVPR03NzuGpe0juSKNY2XHGeEZZ6mSFxvqSmSQM2t25zIi3JzUZxJFMvJqiBnggkKuIM5zKHsN+6g9vhlVWS5y/5izyqvn+iAweIArRzLLMRhNKORX991W1FCCq5Vzqw+RcwQ5BPDERfegK5yTU/dwtuaegLb6WCO94tmQ+yt2SltsVddv4l9nUPhGWXTKwJzvoU/dgj73EKpBE655f8zKuL3rU+oTMw6JSxPvGcu9kXdwX9QdrUJRE/zGoTPp+TT1K17Y/yqplSeZGTilTQLdWO8RzAqaykTf63uW1h3Eew7BKBp579in2CpteCTmXovG1TmiXAahkWvOLlOdl54w114IRac3suo3c5JlYXkjDc16SyGoYZ5xGASBlLpcMBrQjGr7wh08wIUmrYGcwtqrHk9RY3GbZaruoqSqCZVShqNt+2HTGoWapeG3sjC0/Q++/ozMLQjB3gND9u/X/NqiXgsGba/POCTDcR0Q5xFDtGtEq20+tl4McgolozqbMKcQ/jLiD8wKbpuF7KRxZGZQxxE1Nwp+tj542XhgpdDwWMy9OKjbPnxKuZKh7tEoZAoGObdW3fVysWHqMD/2pBRzqqiO9DNmmetzpXED7f1wVdpxzE6DavAMZA5tQ1Qjg5yRy4QOa4l0Bp1RR3lTJd42VyZ6eDlKq5rxdLLuMHIoxi2yze/yRkAQBJQhIzEWpWNq7FqVz6vlXA6HTFqqkrhS7o68neqWmmtarvN6RhAEHou5DzAb1EuxIGQ2433HtMq+Psec0YHsTyvh6y0ZONiocXPU4OKgsZx/mM8INuu30hSdwMXpfWmVGQTY+TLQ35Hk7AoWT2orQHkpKporadA3WvwJxY2liIg9MuMwiSLFlY2XLMsrAcrQUeiOrMWQ8zuqwTdds+tassYlw9E7iKJIQ0Mtzc0NmNqpfHaOsjIZJlPfED1rDxVQ0nTmssd1lasdt0wmx8rKFltbhz5V3Kcjg3EOa6UV1sr2pcSt1AoWTwzh4w0ngHrGDW794h7uOZRfz2zn64yfeWTwPZaZ3m8F+/ghcw0JfuOIGTCEb7dlUVbT3KlqcaIo8lnaN5Q0lvLymD9jpbCiqMEcUeVt270zjvQz1Xy3PYuK2haLNpJEW2QOnsjcgtBnXWvDYQ6l7W0fxw1rOKqryxEEAWdnD+RyxSVfbgqFDEMfqEl9rbmacYuiiNFooL6+hurqcpydr7wedF9kZKQHO48VklVQa1mmOoe7tSu3D1zE1+k/8nX6KpaGLyalIo0fM9cCZhHKe0Mn8+22LJKzKzr1cj5Ve4YzdWZ13b1FB5niP4HCxmKUMqUl0ulq0emNfLLxJIfSy3C2V/PgnAiGX6L4lIQZZcgotPu/wVhdiNzpvJacKJrQHvgBZchI5K6B3XpNk2XG0buBLjesj0Ona8HR0QWFQtmnvoj7A4IgoFAocXR0QafrfPnc6wVBELhr+kCig12IHtD2xT3aexgzg6ZyoOQwX5z4jv+mfUOAvR/TAiZR2FCMrS34u9uyK7nokhIlF7I9fzfWCiuC7P3Zmb8Xo8lIUUMJ3jaelqivq8FoEvl4/QkOp5cxf2wQ/3hgJCMjPVvJw0u0RTFgBMgU6E+0LqttPJOMPmUzLTs+QTQZuvWa53WqJOd4LyEidMNDJ3FpzPe369n61wM+brY8vTgGW6v2hT5nBk5htNdwkkqP4qh24OHBdxPpMggRkeyaU0yJ96OwvJETpzt2rlY0V5FcnspYn5FMDZhEtbaGY+WpFDYUd8sylSiKfLL2OIczy7l1cihzxwZ1WCZW4jwyawcUISPQZ+xuldOhO/4rKFSYqgvQp23v4AwdY6opoTnxHZo2v2nZJjbXmstFd1Iev6e4YZeqJCR6EkEQuG3gAnxsvYh2jcBOZYtGoUEpU5JZncP8iAhW/ZZDYlI+kUHOlzzPzoI9CILABN/R2KvscLVyYWNuIg36xm5xjG8+mMeGPblMG+bHtGGST6OrqKKnY8jci+7kTtRDZmGsOIOxOB31iMUYCk+gPbwaRcjILkVBiS0NaI+sNRsd0ex/NdWUIHP0RGzu/axxuKFnHH2Pm2+ew5Ili1i27HbuuONm1q9fY9lnNBqZP/8mnnvuf1q1OXLkEGPHxvPVV5+32nbffUstf48dG8+yZbexbNlt3HbbQp5/fgW5ua2rA+7atZN7772DJUsWsXjxPN566w10uvNFoG6+eQ7z5k3HaDwfSLBp03rGjo3np5++765b0K+Qy+RM9BuDy9mCWUqZggEOgWRW51gKPB0/VUlRRfsZyM2GZvYVHSTOPQZHtcPZjO2xlJ6VmrnaUNzqei2rduQwJsabxQmdj/CSOI/cxR+5dzj6tK2IJsPZ2YYa5aAJqEcvAb0O3cEfO3Uuc/tEGr7/X/RpW1EOGof1/L8CYMhLNh/TXNvrobggGY4+x0svvcoXX3zLiy++wuuvv0JFhfklceDAflxd3UhJSaaqqnUOgIuLKz/88C319ZcWL3v//c/44ovv+OabVcTGDuWRR+6jqMgsn3Hs2BHefPNf/PnP/49vvvmJlSt/oKyslDfeePWi67hx8OD5csGbNq0nLOz61Q3rDUKdBlDUWEK9roGJsT4o5DK2HDpfVjarOofNp7ex+fQ2vkn/Ca1RR4LfOMv+kZ7xWCnMkVhXu1SVX1aPCMwZGyz5M64C1eDpiI3V6FO3YMg5gHLQeAS1DXJHb5TR09Bn7MZY1raM84UYitJp/PEvaPd/g9w1EOtFf0cz7m7k7gOQOflgyDsGnMsalwyHxCUIDg7Bzs6e8nJzXYWNG9cyf/5Cxo+fyObNG1sd6+rqxqRJk/n66y8ue16ZTMb8+TczYsRIVq82l8j87LOPWLbsXgYMMH91qtVqnn12Bdu2JVJScr6e+cyZs9m0aQMAhYUFtLS0WNpIdI5z9dSzak5ZCjztSy2hvkmHzqjj4+MrWX/qV9af+pUjZSlEugxqlaejUaiZ6j+BAHu/NioBXaWowiwr4udxY0jR9BRyv8HIHDzR/v4DmEytKgeqh85FsLJHe3jNJduLLQ00b3kHRBNWM57GauYfkTufXzZU+MdgLM5E1DVh6gNyIyD5OCzsPV7MnpTiNtsFAToR+NIhYwd7MSa6a+vRKSnHcHBwJCQkjJqaGg4fPsSf//wC/v6B/OtfL7NkyV2tjl+27D7uuus2br75tk6dPyIiiqSkAwDk5GTx+ONPt9rv4OCAt7cvOTnZeHqa+x4bG8/q1auoq6tj8+aNzJgxk4yM9C6N60YnwM4XlVxFVvUphroPZuowP3YlF7HzWBGOAcU0Gpp4KvYhgh0CAdqNmpoemMD0wISr7ktxZSP21krsbVSUN2mv+nw3KoIgQxk9De2eL1EEDkVmfz78XFBZoYycjO7QaozVRcidvNu01yb9BLpmrOb8Cblz22ReuX8MJG/CkJcC2kZpxiHRlr/85X+5/faFLF/+IA888DBKpZJff93ImDHjsLa2YfDgIRgMRlJTU1q1c3Z2Ye7cBXz++Sedus6FYaCdNYyCAAkJU9m2LZGtW39l6tQZnR6XhBm5TG72c9SY9a58XG0I8XHgaFY52/N342/nS4hjMHKZHLlM3qOh4sWVTXi59G+BwmuFMmwMipBRqOIXtt0XPgnkCvTHE9vsM1acQX9yJ8rIye0aDQC5RwiobdBnmMtL9AXDIc04zjImuv1ZwbVOAHzppVcJDg5h+/at/OMf/4/o6Bg2bVpPdXU1N988B4CGhgY2blxHVNTgVm2XLLmLO+5Y1Kl6JSdPniA42LxsEhISSlracUJDz1euq62tpaiowHLMOWbMmMVDD91NTEwsDg5dLwMrYV6uWpvzC3W6euxVdgwKcGLziSSUTRXcc1att6cRRZGiikYpya+bEBRqrBIeanefzMoeZegY9Fl7UQ1fhExjXhoURZGWvSsRNLao4+Zf+twyOQq/aAzZ5hUCwbr3DYc04+ijJCRMYdiwkfzzn3+nvr6etWs3s2rVelatWs/Kld+zY8dWWlpaJ9fZ2tpy66138sUXn13yvCaTiXXrVnPgwH7mz78ZgLvvvp8vvviMnBxzDRStVsu//vUPJk2agpdX66m1j48vDzzwKHfffX83j/jGweLnqDbPOsL9HZF55GIjtyPWfXBHTbuNukYdTVoDXi7W1+R6NzrK6Glg1KM/scOyzZC1D1NpNuoRixHUHc/8FP4xnMuJkml633BIM44+zMMPL+euu25jxoyZrb5C3dzcCQsbxI4dW/HwaB1Zs2jRYlat+q7NuR555F4AtFodAwcO5P33P8Xb2yyTMHRoPE899UdeeulvaLVa9HoDEyZM4IEHHm23X/PmtZ2OS3QeP1sfbJU2fJexmlpdPf72fsgdqnA3DrtmKsZFZ0vJertKS1XXArmTD3LfKPRp21AOHIfu6Hr0J3cicx+AIuzyJQ8UvtEWh6tg3fvOcUHsjOZBP+DimuMlJWfw9Lx8yUtJq+rq6Ox97gtcyzrrxY2l/JS1npNVmcgEGaJRwKVwNv9v2bWpm7L9SAFfJWby+mNjCAt2ve5qhl8t1/J/fQ5LDXpBDogoIyahjluAoOlcdFzTun9gLMnE9p4PEJRtlZs7Q3fVHJdmHBISvYCXjQfLh9xPWmUGG079irLJi7RiLY0temw0PS8nUVTRiEYlv2ShJonuR+4TidwvGgQ56hGL242w6ghl+ETzrENxsWD/tUcyHBISvUiky0AiXQaSkVdN6v6jZObVEBvm1uPXPRdRJQl8XjsEQcD6pj9ccXtl6GiUoaO7sUdXjuQcl5DoAwR7O6BUyEjPq7km1yuqbMTbVXKMS1wZkuGQkOgDKBUyQnwcSM/r+VKkTS16aht0eEs5HBJXiGQ4JCT6CIP8Hckva6ChWd+j1zkXUSUl/0lcKZLhkJDoIwwKMKvoZvTwrKP4rBqvl7RUJXGFSM7xPsTNN89BpVKhVKpoaWkmKCiYO+5YRnR0DADJyUf54IN3qa6uxmg0EBMTy+OPP23J4F6+/EFOn87lhx/WYm1tbdl2++1LGTNm3CWvK9E3CPKyR6WUsX7faVJyzArIkUHODA/v3uzu4somFHIZbg6Xr3cuIdEe0oyjj3FOVv3779dw002zeeaZJ0lLS6WgIJ8///kZHnzwUb777me+/34N9vb2/PnPz7Zqr9Fo+O67r3qp9xJXg0IuY/xgb+qb9KTmVpGUXsaXmzPQd3MeUVFlI57O1shkUkSVxJUhGY4+zIQJCcybt4hvv13Jl19+xqxZ84iNjQPM8uiPPvokRUWFHD6cZGlz5513s3r1Kmpqrk10jkT3smRqGK8/NobXHxvDI/OjaNIaOH6q8vINu0BRhRRRJXF19ImlqrVr1/LJJ5+Qk5PDihUruPPOOy37cnNz+dvf/kZdXR06nY6ZM2fy+OOPd3sf9Jl70WfsarNdEASuNrleOXA8yk7ICrRHREQUe/fuQq3WsGzZfa32KRQKwsIGkpOTTVzcMMAsRzJjxiy+/PJTnnjiymPGJXqfiEAn7K2V/J5WwtALcjtMJhGt3oiVuuuPr05vpLK2hbFdlPmXkLiQPjHjCA8P580332T27Nlt9r322mtMnz6dtWvXsmrVKn7++WdSUlLaOUt/xWy0OjJeFydx3XnnMrZs+ZXS0pIe7ZlEzyKXyRgW7sGx7EqaWgyW7V9sTmfFx7+j1Rs7aN0+B06WIgI+blJElcSV0ydmHGFhYYB5+eViBEGwlERtaWlBEAScnZ27vQ/KsDHtzgp6W6vq5MkTBAUNwMrKirS044wfP9Gyz2AwkJmZ0Uap1sHBkUWLFvPppx9e495KdDcjIzzYdriAw5lljBvsTXZBLbvPFhzbn1bCxCE+nT7XidNVfLk5g0H+jsSEuPZUlyVuAPqE4eiIFStW8PDDD/PNN99QV1fHs88+i69v+wVPOuJisa6yMhkKRecmXJ09rjuQy8/3a9eunaxZs4p///s97O3teeCBexgzZixDh8ZhMpn44IO3iY0dSlRUFGA2snK5gEIh4447lrJ48XxkMrllW1fpjnHLZDLc3K6f0qR9ra+urrZ4bUrnaFYl8yaF8f1Xh3G212Bvo2L7kUIWTR7YKSd3blEt761Oxc/DjhceHI2NVWs9rL427mvBjThm6J5xXxPDsWDBAoqKitrdt2/fPuTyS0tJf//998ybN4/777+fsrIyli5dSlRUFDExMV3qw8XquCaTqVMziWs94/jTn56xhOMGBgbx2mtvMWhQJGCOuPrPf96hpqaG6upKRo4cw9/+9qKlf6IoYjSKGAwmlEo1d955N2+++ZplW1fornGbTKbrRnm1NxRTO0P8QDc27D/Nf9ceJ6eglgfnRiCK8PH6E+w4eIbBA1w6bF/XpOOFzw6iUclZviCKpoYWmhrO13Lpq+PuSW7EMcN1po67evXqK267cuVKtm7dCoC7uzsjR44kKSmpy4bjemDVqvUd7o+NjeODD8xFmo4fT+b551dw8OB+Ro0aC8C7737U6vhFi25l0aJbe6azEteMkZEerN93mrV7cgnzc2REuAdGk8iPO7JJTMq7rOFIO1VFTYOOP905FGf7K5PjlpC4kD7hHO8IX19fdu8219ptaGjg8OHDhIaG9nKvep/o6Bh+/nmjxWhI9F+8XGwI8LBDEOCOqWEIgoBCLmNynC8nTldTUNbQYfuC8gYUcoEgr96vHCfRP+gThmPDhg2MHz+ezZs389ZbbzF+/Hiys81lTP/5z3/y3XffMXfuXBYvXsyMGTOYMGFCL/dYQuLacuf0MB6aG4mf+/mlgwlDfFApZSQm5XfYNr+8AS8XGxTyPvG4S/QD+oRzfPbs2e2G4gJERUXx3XdtS6FKSNxIDPB2YIB365KhtlZKxkR5sTuliNunhF4yr6OwvJFB/o7XopsSNwjSJ4iExHXM8HB3DEaRE6er2t3f0Kynul6Lr3vnypNKSHQGyXBISFzHhPg6YK1WkJzdvixJYbnZ/+HrJhkOie5DMhwSEtcxcpmMqGBnUk5VYmpHXaCg3CyhLhkOie5EMhwSEtc5MSGu1DXqOFPSNj6/oLwBG40CR1tVL/RMor/SJ5zjEmY6qsexadN63n77dTw9vTEY9Hh5efPcc3/FxcUsHWEymfjqq8/ZtGkD8rPRM4sW3crChbcAUFxcxC23zGX+/EX88Y9/smy7//6lbNy4rXcGLNEtRAe7IAiQnF3RJuS2oKwBXzfbNnpmEhJXgzTj6GNcqh4HQHz8cD7//BtWrvwBa2tr/vvfjy3tvvzyMw4e/J2PPvqcr79exRtvvMvPP//Ihg1rLcdYWVmze/dOCgsLrvm4JHoOWyslA3wcSM5p7ecwiSIFFY2SY1yi25EMRx/mwnocFyIIAkOGxFnUb7VaLStX/pdnnlmBvb35i9PDw5PHHnuSzz47n02uUim57balfPTRf67dICSuCTEDXDhTUk91vdayrbK2Ba3OiK+khCvRzUhLVWc5UHyY/cVJbbYLAlxlOQ5GeQ1jhFfcFbU9V49j9OjzGeJ6vZ79+/eSkDAVgMLCfJRKJQEBga3aRkZGU1ZWSkPD+czihQtvYcmSRWRlZWBre2OKvPVHYga48tNvpzh+qpLxMd4AloxyacYh0d1IM44+z3mrdejQQe6+ewmzZ0+hpqbaYjguZ9guXN5Wq9Xcfff9fPjhez3RWYlewsfNBhd7NcnZFZZtBWdDcX1cpRmHRPcizTjOMsIrrt1ZQV+pxwFmH8dLL/2LpqZGnn56OZ9++gGPPvokvr5+6PV6zpw53WrWkZZ2nKCgYGxsbKmrq7NsnzlzDt9+u5Lk5KPXejgSPYQgCAwe4Mq+1BLqmnTYW6soKG/EzVGDRiU95hLdizTj6MPs3m2ux3HbbXe22m5tbcMzz6xg9epVVFRUoFarueOOZbz22j8sBqK0tIQPPniXxx57qs155XI5DzzwiFToqZ8xYYg3oijy1o8paHVGCsobpPwNiR5B+hTpY/zlL//bph5HZGQUZ87ktjouJCSUSZOm8PXXX/Dkk3/g7rvvRyaT8eCDywBzqO0rr7zOyJGj272Oue2XNDU19viYJK4N/h52PDQ3kndXH+c/a1IprWomfqB7b3dLoh8iiB0Vs+5HXFzIqaTkDJ6eAZdt19tLVVeCKIp8/PH77Nu3h7fffh97e4fLN7qI7hp3Z+9zX6C/FPfZcaSAlYmZADw6P4r4QR0bj/4y7q5wI44Zuq+Qk7RU1Q8RBIEHH3yUzz//5oqMhsT1zaShvswaFYAgQKCnFDkn0f1IS1USEv2QRRMGMH24P7YX1RaXkOgOpBmHhEQ/RTIaEj3FDWw4BETx+vJdXG+Y76+kkSQh0d+4YQ2HSqWhpqYCg0HPDRIfcM0QRRGDQU9NTQUqlaa3uyMhIdHN3LA+DicnNxoaaqmqKsVkMl7yOJlMhsl0481MrnbcMpkcKytbbG0l57yERH/jhjUcgiBgZ+eInV3HtZilsD0JCQmJ1tywS1USEhISEleGZDgkJCQkJLrEDbNUJZNdeXTP1bS9nrkRx30jjhluzHHfiGOGzo+7o+NuGMkRCQkJCYnuQVqqkpCQkJDoEpLhkJCQkJDoEpLhkJCQkJDoEpLhkJCQkJDoEpLhkJCQkJDoEpLhkJCQkJDoEpLhkJCQkJDoEpLhkJCQkJDoEpLhkJCQkJDoEpLhuAS5ubnceuutTJ8+nVtvvZXTp0/3dpd6hOrqah544AGmT5/OnDlzWL58OVVVVQAcO3aMuXPnMn36dO69914qKyt7ubfdz7vvvsvAgQPJzMwE+veYtVotzz//PNOmTWPOnDn89a9/Bfr/b33Hjh3Mnz+fefPmMXfuXBITE4H+Ne5XX32VhISEVr9l6HiMVzV+UaJdli5dKq5Zs0YURVFcs2aNuHTp0l7uUc9QXV0t/v7775a/X3nlFfFPf/qTaDQaxSlTpohJSUmiKIrie++9Jz733HO91c0eITU1VbzvvvvESZMmiRkZGf1+zC+++KL48ssviyaTSRRFUSwvLxdFsX//1k0mkxgfHy9mZGSIoiiKJ0+eFIcMGSIajcZ+Ne6kpCSxqKjI8ls+R0djvJrxS4ajHSoqKsS4uDjRYDCIoiiKBoNBjIuLEysrK3u5Zz3P5s2bxWXLlonJycnirFmzLNsrKyvFIUOG9GLPuhetVisuXrxYzM/Ptzxs/XnMDQ0NYlxcnNjQ0NBqe3//rZtMJnH48OHioUOHRFEUxYMHD4rTpk3rt+O+0HB0NMarHf8No47bFYqLi/Hw8EAulwMgl8txd3enuLgYZ2fnXu5dz2Eymfj2229JSEiguLgYb29vyz5nZ2dMJhM1NTU4OnZc/Op64K233mLu3Ln4+vpatvXnMefn5+Po6Mi7777LgQMHsLGx4cknn0Sj0fTr37ogCPz73//m0UcfxdramsbGRj766KMb4hnvaIyiKF7V+CUfh4SFF198EWtra+68887e7kqPcvToUVJTU1myZElvd+WaYTQayc/PJyIigp9//pk//vGPPP744zQ1NfV213oUg8HAhx9+yH/+8x927NjB+++/z1NPPdXvx93TSDOOdvDy8qK0tBSj0YhcLsdoNFJWVoaXl1dvd63HePXVVzlz5gwffPABMpkMLy8vioqKLPurqqqQyWTX/Zc3QFJSEjk5OUyePBmAkpIS7rvvPpYuXdpvx+zl5YVCoWD27NkAxMTE4OTkhEaj6de/9ZMnT1JWVkZcXBwAcXFxWFlZoVar+/W4oeP3mCiKVzV+acbRDi4uLoSHh7NhwwYANmzYQHh4eL+Zwl7MG2+8QWpqKu+99x4qlQqAqKgoWlpaOHToEADfffcdM2bM6M1udhsPPvgge/bsYfv27Wzfvh1PT08+/fRT7r///n47ZmdnZ0aMGMHevXsBc0RNZWUlgYGB/fq37unpSUlJCadOnQIgJyeHyspKAgIC+vW4oeP32NW+46RCTpcgJyeH5557jrq6Ouzt7Xn11VcJDg7u7W51O1lZWcyePZvAwEA0Gg0Avr6+vPfeexw5coTnn38erVaLj48Pr732Gq6urr3c4+4nISGBDz74gLCwsH495vz8fFasWEFNTQ0KhYKnnnqKCRMm9Pvf+rp16/j4448RBHNFuyeeeIIpU6b0q3G/9NJLJCYmUlFRgZOTE46OjmzcuLHDMV7N+CXDISEhISHRJaSlKgkJCQmJLiEZDgkJCQmJLiEZDgkJCQmJLiEZDgkJCQmJLiEZDgkJCQmJLiEZDgmJHmTWrFkcOHCgW8/53HPP8eabb3brOSUkuoKUOS4h0YNs3Lixt7sgIdHtSDMOCQkJCa5qpC4AAAQ2SURBVIkuIRkOCYlOUFpayuOPP87IkSNJSEjgyy+/BOCdd97hiSee4KmnniI2NpYFCxaQnp5uaZeQkMC+ffsASElJYeHChQwdOpTRo0fzz3/+03Lctm3bmDVrFvHx8SxdupScnBzLvhMnTrBgwQJiY2N56qmn0Gq1rfq2Y8cO5s2bR3x8PLfddlur60tI9AjdrQcvIdHfMBqN4oIFC8R33nlH1Gq1Yl5enpiQkCDu2rVLfPvtt8WIiAjxl19+EXU6nfjJJ5+IkyZNEnU6nSiK5voIe/fuFUVRFBcvXiyuXr1aFEVzfYyjR4+KoiiKp06dEmNiYsQ9e/aIOp1O/Oijj8QpU6aIWq1W1Gq14sSJE8X//ve/ok6nE3/55RcxIiJCfOONN0RRFMW0tDRx5MiR4rFjx0SDwSD+/PPP4qRJk0StVtsLd0riRkGacUhIXIbjx49TVVXF8uXLUalU+Pn5sXjxYjZt2gRAZGQkM2bMQKlUcs8996DT6UhOTm5zHoVCQV5eHlVVVdjY2DBkyBAANm3axIQJExgzZgxKpZL77ruPlpYWjh49SnJyMnq9nmXLlqFUKpkxYwbR0dGWc37//ffceuutxMTEIJfLWbBgAUqlkmPHjl2bmyNxQyI5xyUkLkNhYSFlZWXEx8dbthmNRuLj4/H29sbT09OyXSaT4eHhQVlZWZvzvPzyy7z99tvcdNNN+Pr6snz5ciZNmkRZWVmrAlLnZO1LS0uRy+V4eHhYBPqAVscWFRWxZs0avvrqK8s2vV7f7vUlJLoLyXBISFwGLy8vfH19SUxMbLPvnXfeoaSkxPK3yWSitLQUd3f3NscGBgbyxhtvYDKZSExM5IknnuDAgQO4u7uTmZlpOU4URUv1NkEQKC0tRRRFi/Eo+v/t3a2qKmEUxvE/uB0EwQsQRLQZ/LwBw2DwArwHbTaTRYuioDBlMAiarBbDJMVgVrHYpmywi4ijuMMB4bA5bIaz2Sec53cBi/W+5WGtst7ficVir96q1Sq1Wu27ny3yR1pViXwhk8kQDocZjUZcr1cejwfH45HdbgfA4XDAcRzu9zuTyQTDMMhms5/qzOfz13GoSCQC/JouyuUyq9WKzWaD53mMx2MMwyCfz5PL5Xh7e2M6neJ5Ho7jsN/vXzUrlQqz2Yztdsvz+eRyubBcLjmfzz/zOfJf0sQh8oVAIIBt23S7XUzT5Ha7kUgkqNfrAJimyWKxoNFoEI/HsSyLYDD4qc56vabT6XC9XolGowwGA0KhEMlkkl6vR7vd5nQ6kUqlsG37dVTLsiyazSbD4ZBisUipVHrVTKfTtNttWq0WrusSCoUoFAq/rdVEvpvucYj8BcuycF2Xfr//r1sR+TFaVYmIiC8KDhER8UWrKhER8UUTh4iI+KLgEBERXxQcIiLii4JDRER8UXCIiIgvCg4REfHlA5nzGGCxyS1JAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env.plot_state()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "mPbAK3JmrEwU",
        "outputId": "5ffc07c4-3224-4ad9-db74-43b7dc515c9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<b>Environment (2-back):</b><br /><pre><b>Stimuli:</b> 5151524412154532321252021212020</pre><pre><b>Actions:</b> ...............M...............</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# N = 2\n",
        "# n_episodes = 1000\n",
        "# episode_steps= 100\n",
        "for key, val in logs_dict.items():\n",
        "    logs=logs_dict[key]\n",
        "    reward_ma=moving_average(logs['episode_return'],10)\n",
        "    plt.plot(reward_ma)\n",
        "\n",
        "plt.ylabel(\"cumulative reward\")\n",
        "plt.xlabel(\"episode\")\n",
        "plt.legend(['RANDOM','DQN','DRQN'])\n",
        "# because episode_steps too large, \n",
        "# DQN learn the best policy within the first episode \n",
        "# so reward is not increasing by episode"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "PoexUzo1ybkM",
        "outputId": "6916d3a9-d36e-44c6-ada8-72bbd83bb85d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fab611273d0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# N = 2\n",
        "# n_episodes = 1000\n",
        "# episode_steps= 32\n",
        "for key, val in logs_dict.items():\n",
        "    logs=logs_dict[key]\n",
        "    reward_ma=moving_average(logs['episode_return'],10)\n",
        "    plt.plot(reward_ma)\n",
        "\n",
        "plt.ylabel(\"cumulative reward\")\n",
        "plt.xlabel(\"episode\")\n",
        "plt.legend(['RANDOM','DQN','DRQN'])\n",
        "# episode_steps is small and dqn is learning by episode"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "7lg4y9-loCS7",
        "outputId": "58af0a92-0002-4943-c476-07d1fa5d0162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fab60eebb10>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# N = 5\n",
        "# n_episodes = 1000\n",
        "# episode_steps= 32\n",
        "\n",
        "for key, val in logs_dict.items():\n",
        "    logs=logs_dict[key]\n",
        "    reward_ma=moving_average(logs['episode_return'],10)\n",
        "    plt.plot(reward_ma)\n",
        "\n",
        "plt.ylabel(\"cumulative reward\")\n",
        "plt.xlabel(\"episode\")\n",
        "plt.legend(['RANDOM','DQN','DRQN'])\n",
        "plt.title(\"5-back\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "Rv5Hz3emoC4C",
        "outputId": "9afb1f43-d842-43f4-d364-0a32efe38639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f66949bc190>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## error pattern"
      ],
      "metadata": {
        "id": "kQodUSba-nRE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### error statistics"
      ],
      "metadata": {
        "id": "Y3SxB2-efW-L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logs_dqn=logs_dict['DQN']\n",
        "# dataframe\n",
        "print(logs_dqn.tail(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99A5IpgraSm8",
        "outputId": "62e0a0aa-7e7f-4c97-8409-e847e57fed7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     episode_length episode_return  steps_per_second  episodes  steps\n",
            "990              32           -9.0        349.108040       991  31712\n",
            "991              32           -9.0        396.365636       992  31744\n",
            "992              32          -13.0        358.983233       993  31776\n",
            "993              32           -8.0        354.385210       994  31808\n",
            "994              32           -6.0        310.416852       995  31840\n",
            "995              32           -4.0        328.329284       996  31872\n",
            "996              32           -8.0        340.878212       997  31904\n",
            "997              32           -4.0        396.796859       998  31936\n",
            "998              32           -9.0        348.173441       999  31968\n",
            "999              32           -5.0        348.660945      1000  32000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "6K_MB5VVFmxf"
      },
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