fairness-in-MARL.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g54TEFh9jWE7",
        "outputId": "4c5092e3-5a97-4eb8-a63f-229437951e2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 21.8 MB 20.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 43.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 448 kB 50.4 MB/s \n",
            "\u001b[33mWARNING: gym 0.17.3 does not provide the extra 'box_2d'\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "pip install pygame Box2D box2d-py gym torch opencv-python  gym[Box_2D]  --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "v01Sz0rJjWFI"
      },
      "outputs": [],
      "source": [
        "import gym\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from gym import spaces\n",
        "from collections import deque, OrderedDict\n",
        "import collections\n",
        "import itertools\n",
        "import math\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import random\n",
        "from copy import copy,deepcopy\n",
        "import copy\n",
        "from IPython import display\n",
        "plt.style.use('ggplot')\n",
        "import glob\n",
        "from PIL import Image\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "FcPybFNNjWFL"
      },
      "outputs": [],
      "source": [
        "#Grid Environment\n",
        "def grid(m,n):\n",
        "    grid = np.zeros((m,n))\n",
        "\n",
        "# 5*5 Grid World\n",
        "class GridEnvt(gym.Env):\n",
        "    metadata = { 'render.modes': []}\n",
        "\n",
        "    class Spec:\n",
        "        def __init__(self, name):\n",
        "            self.id=  name\n",
        "\n",
        "    def __init__(self, m, n, n_agents, agents, goals, entities, resource_capacity, priority, priority_levels, max_timesteps, verbose):\n",
        "        self.grid = np.full((m,n), -1)\n",
        "        self.m = m\n",
        "        self.n = n\n",
        "        self.goals_given = goals\n",
        "        self.agents_given = agents\n",
        "        self.goal_pos = goals\n",
        "        self.agent_pos = agents\n",
        "        self.priorities = priority\n",
        "        self.set_priorities()\n",
        "        \n",
        "        self.resource_pos = [2,2]\n",
        "        self.set_gridvals()\n",
        "        self.reward_fun = [10, 50]\n",
        "        self.state = []\n",
        "\n",
        "        self.observation_space = spaces.Discrete(m*n)\n",
        "        self.action_space = spaces.Discrete(4)\n",
        "        self.n_agents = n_agents\n",
        "        self.is_agent_silent = [False for i in range(self.n_agents)]\n",
        "        self.timestep = 0\n",
        "        self.max_timesteps = max_timesteps\n",
        "        self.reward = [0 for i in range(n_agents)]\n",
        "        self.isClipped = False\n",
        "        self.entities = entities\n",
        "        self.actions_ = {0:\"Left\", 1: \"Right\", 2:\"Up\", 3:\"Down\"}\n",
        "        self.dones = [False for i in range(n_agents)]\n",
        "        self.resource_collected = [False for i in range(self.n_agents)]\n",
        "        self.power_for_agents = [0 for i in range(self.n_agents)]\n",
        "        self.resources_taken = [0 for i in range(self.n_agents)]\n",
        "        self.water_consumption_fun = lambda x: x*10\n",
        "        self.resource_capacity = resource_capacity\n",
        "        self.resource_capacity_ = resource_capacity\n",
        "        self.water_needed = []\n",
        "        self.damage = [0] * self.n_agents\n",
        "        self.resource = [0] * self.n_agents\n",
        "        for i in self.entities: self.water_needed.append(self.water_consumption_fun(i)) #water needed to put out fire\n",
        "        self.spec = self.Spec('GridWorld')\n",
        "        \n",
        "        self.prioritylevels = priority_levels\n",
        "        self.collect_resrcs()\n",
        "        self.verbose = verbose\n",
        "        self.agent_res_count = 0\n",
        "        self.goals_count = [0 for i in range(self.n_agents)]\n",
        "        self.entities_saved = [0 for i in range(self.n_agents)]\n",
        "\n",
        "    def set_gridvals(self):\n",
        "        self.grid = np.full((self.m,self.n), -1)\n",
        "        self.grid_vals_dict = {}\n",
        "        self.grid_vals_dict[tuple(self.goal_pos[0])] = 100\n",
        "        self.grid_vals_dict[tuple(self.goal_pos[1])] = 40\n",
        "        self.grid_vals_dict[tuple(self.goal_pos[2])] = 60\n",
        "        self.grid_vals_dict[tuple(self.resource_pos)] = 160\n",
        "        for i in self.grid_vals_dict:\n",
        "            self.grid[tuple(i)] = self.grid_vals_dict[i]\n",
        "        self.grid[tuple(self.agent_pos[0])] = 90\n",
        "        self.grid[tuple(self.agent_pos[1])] = 30\n",
        "        self.grid[tuple(self.agent_pos[2])] = 50\n",
        "    \n",
        "    def set_priorities(self):\n",
        "        priority = deepcopy(self.priorities)\n",
        "        agent_pos_t = []\n",
        "        goal_pos_t = []\n",
        "        for i in range(len(self.priorities)):\n",
        "          k = np.argmax(self.priorities,0)\n",
        "          agent_pos_t.append(self.agent_pos[k])\n",
        "          goal_pos_t.append(self.goal_pos[k])\n",
        "          self.priorities[k] = -1\n",
        "        self.agent_pos = agent_pos_t[::-1] #assigned agents position based on priority\n",
        "        self.goal_pos = goal_pos_t[::-1]\n",
        "        self.priorities = deepcopy(priority)\n",
        "    \n",
        "    def collect_resource(self, agent):    # not using. \n",
        "        if self.prioritylevels[agent] == \"H\":\n",
        "            self.power_for_agents[agent] = self.water_needed[agent]\n",
        "            \n",
        "        elif self.prioritylevels[agent] == \"E\":\n",
        "            resource_remain_for_agents = self.resource_capacity-sum(self.water_needed[:agent])\n",
        "            self.power_for_agents[agent] = resource_remain_for_agents/2\n",
        "        self.resource_capacity-=self.power_for_agents[agent]\n",
        "\n",
        "    def collect_resrcs(self):\n",
        "        resource_for_highpriorirty_agents = 0\n",
        "        for agent in range(self.n_agents):\n",
        "            if self.prioritylevels[agent]==\"H\":\n",
        "                resource_for_highpriorirty_agents+=self.water_needed[agent]\n",
        "                \n",
        "        for agent in range(self.n_agents):\n",
        "            if self.prioritylevels[agent]==\"H\":\n",
        "                self.power_for_agents[agent] = self.water_needed[agent]\n",
        "            elif self.prioritylevels[agent]==\"E\":\n",
        "                self.power_for_agents[agent] = int((self.resource_capacity-resource_for_highpriorirty_agents) / self.prioritylevels.count(\"E\"))\n",
        "\n",
        "\n",
        "\n",
        "    def reset(self):\n",
        "        self.timestep = 0\n",
        "        self.goal_pos = self.goals_given\n",
        "        self.agent_pos = self.agents_given\n",
        "        self.set_priorities()\n",
        "        self.set_gridvals()\n",
        "        self.reward = [0 for i in range(self.n_agents)]\n",
        "        self.dones = [False for i in range(self.n_agents)]\n",
        "        self.power_needed = []\n",
        "        for i in self.entities: self.power_needed.append(self.water_consumption_fun(i)) #power needed to put out fire\n",
        "        self.power_for_agents = [0 for i in range(self.n_agents)]\n",
        "        self.entities_saved = [0 for i in range(self.n_agents)]\n",
        "        self.resource_collected = [False for i in range(self.n_agents)]\n",
        "        self.is_agent_silent = [False for i in range(self.n_agents)]\n",
        "        self.resources_taken = [0 for i in range(self.n_agents)]\n",
        "        self.goals_count = [0 for i in range(self.n_agents)]\n",
        "        self.resource = [0] * self.n_agents\n",
        "        self.resource_capacity = self.resource_capacity_\n",
        "        self.agent_res_count = 0\n",
        "        self.collect_resrcs()\n",
        "        observation = self.grid\n",
        "        return observation\n",
        "    \n",
        "    def _compute_distance(self, x, y):\n",
        "        dist = np.abs(x[0]-y[0]) + np.abs(x[1]-y[1])\n",
        "        return dist\n",
        "    \n",
        "    def is_collided(self, agent, pos):\n",
        "        for i in range(self.n_agents):\n",
        "            if i==agent:\n",
        "                continue\n",
        "            elif np.array_equal(pos,self.goal_pos[i]):\n",
        "                return True\n",
        "            elif np.array_equal(pos,self.agent_pos[i]):\n",
        "                return True\n",
        "        return False\n",
        "    \n",
        "    def is_in_boundary(self, agent, pos):\n",
        "        return not all(0 <= x < self.n for x in pos)\n",
        "\n",
        "    def is_clashing_agents(self, agent, pos):\n",
        "        pass\n",
        "\n",
        "    \n",
        "    def step(self, actions):    #agent_n--> 0: agent 1  1: agent2\n",
        "        info = {}\n",
        "        self.timestep += 1\n",
        "        self.reward = [0 for i in range(self.n_agents)]\n",
        "        self.prev_pos = deepcopy(self.agent_pos)\n",
        "        self.new_pos = deepcopy(self.prev_pos)\n",
        "                \n",
        "        for i in range(self.n_agents):\n",
        "            if not self.is_agent_silent[i]:\n",
        "              if self.dones[i] == True:\n",
        "                  self.is_agent_silent[i] = True\n",
        "                  \n",
        "              if actions[i] == 0: #left\n",
        "                  self.new_pos[i][1] -=1\n",
        "              elif actions[i] == 1: #right\n",
        "                  self.new_pos[i][1] +=1\n",
        "              elif actions[i] == 2: #up\n",
        "                  self.new_pos[i][0] -=1\n",
        "              elif actions[i] == 3: #down\n",
        "                  self.new_pos[i][0] +=1\n",
        "              else:\n",
        "                  self.new_pos[i] = self.new_pos[i]\n",
        "            \n",
        "        # Checking boundaries and other agent goal positions\n",
        "        for i in range(self.n_agents):\n",
        "            if not self.is_agent_silent[i] or not self.dones[i]:\n",
        "              if self.is_in_boundary(i,self.new_pos[i]) or self.is_collided(i, self.new_pos[i]):\n",
        "                  if not self.is_agent_silent[i] or not self.dones[i]:\n",
        "                      self.reward[i] -= 2\n",
        "        self.new_pos = np.clip(self.new_pos, 0, self.m-1).tolist()\n",
        "\n",
        "        #Checking distance\n",
        "        for i in range(self.n_agents):\n",
        "          if not self.is_agent_silent[i] or not self.dones[i]:\n",
        "              if not self.resource_collected[i]:\n",
        "                  prev_dis = self._compute_distance(self.prev_pos[i], self.resource_pos)\n",
        "                  cur_dis = self._compute_distance(self.new_pos[i], self.resource_pos)\n",
        "              else:\n",
        "                  prev_dis = self._compute_distance(self.prev_pos[i], self.goal_pos[i])\n",
        "                  cur_dis = self._compute_distance(self.new_pos[i], self.goal_pos[i])\n",
        "              if prev_dis < cur_dis:\n",
        "                  if not self.is_agent_silent[i] or not self.dones[i]:\n",
        "                      self.reward[i] -= 1\n",
        "              else:\n",
        "                  if not self.is_agent_silent[i] or not self.dones[i]:\n",
        "                      self.reward[i] += 1\n",
        "\n",
        "        # Checking if agent is clashing with other agents\n",
        "        if self.new_pos.count(self.new_pos[0])>1:\n",
        "            if self.verbose:\n",
        "                print(f\"Agents are clashing. Priority given to agent{np.argmin(self.priorities)}\")\n",
        "            self.agent_pos[0] = self.new_pos[0]\n",
        "        else:\n",
        "            #***Moving agent to new position***\n",
        "            self.agent_pos = self.new_pos\n",
        "\n",
        "        #Setting grid values\n",
        "        self.set_gridvals()\n",
        "               \n",
        "\n",
        "        #checking if agents entered goal positions\n",
        "        for i in range(self.n_agents):\n",
        "            if not self.is_agent_silent[i] or not self.dones[i]:\n",
        "              if np.array_equal(self.agent_pos[i], self.resource_pos):\n",
        "                if not self.resource_collected[i]:\n",
        "                  if i==1:\n",
        "                    self.agent_res_count+=1\n",
        "                  if not self.is_agent_silent[i] or not self.dones[i]:\n",
        "                      self.reward[i] += self.reward_fun[0]\n",
        "                  self.resource_collected[i] = True\n",
        "              \n",
        "              if np.array_equal(self.agent_pos[i], self.goal_pos[i]):\n",
        "                  if self.resource_collected[i]==True:\n",
        "                      self.resources_taken[i] = self.power_for_agents[i] \n",
        "                      entities_saved = int(self.resources_taken[i]/10)\n",
        "                      if not self.is_agent_silent[i] or not self.dones[i]:\n",
        "                          self.reward[i] += entities_saved    #may or may not save all entities\n",
        "                      self.dones[i] = True\n",
        "                      self.is_agent_silent[i] = True\n",
        "                      self.goals_count[i]+=1\n",
        "        \n",
        "        observation = self.grid.flatten()\n",
        "        self.state = observation\n",
        "        if self.timestep>=self.max_timesteps:\n",
        "            self.dones = [True for i in range(self.n_agents)]\n",
        "        return observation,self.reward,self.dones,info        \n",
        "    \n",
        "\n",
        "    def render(self):\n",
        "        plt.grid(False)\n",
        "        plt.imshow(self.grid)\n",
        "        plt.savefig(\"frames/\"+str(self.timestep)+\".png\")  \n",
        "    \n",
        "    \n",
        "    def plot_graph(self,x,y,x_label,y_label,title):\n",
        "        plt.plot(x,y,x_label,y_label,title)\n",
        "        plt.show()\n",
        "\n",
        "    def get_state(self):\n",
        "        # 1. goal\n",
        "        # 2. gold\n",
        "        # 3. Agent\n",
        "        layer_goal = np.zeros((self.m,self.n))\n",
        "        layer_subgoal = np.zeros((self.m,self.n))\n",
        "        layer_agent = np.zeros((self.m,self.n))\n",
        "        layer_subgoal[2,2] = 1\n",
        "        c = 1\n",
        "        for agent in range(self.n_agents):\n",
        "          layer_agent[self.agent_pos[agent][0], self.agent_pos[agent][1]] = c\n",
        "          layer_goal[self.goal_pos[agent][0], self.goal_pos[agent][1]] = c\n",
        "          c+=1\n",
        "\n",
        "        return np.array([layer_agent, layer_subgoal, layer_goal])\n",
        "    \n",
        "\n",
        "    def get_state_layers(self):\n",
        "        # 1. goal\n",
        "        # 2. sub-goal\n",
        "        # 3. Agent\n",
        "        c = 1\n",
        "        final_layers = np.empty([0,5,5])\n",
        "        for agent in range(0,self.n_agents):\n",
        "          \n",
        "          layer_goal = np.zeros((self.m,self.n))\n",
        "          layer_subgoal = np.zeros((self.m,self.n))\n",
        "          layer_agent = np.zeros((self.m,self.n))\n",
        "          layer_subgoal[2,2] = 1\n",
        "          layer_agent[self.agent_pos[agent][0], self.agent_pos[agent][1]] = 1\n",
        "          layer_goal[self.goal_pos[agent][0], self.goal_pos[agent][1]] = 1\n",
        "          c+=1\n",
        "          layer = np.array([layer_agent, layer_subgoal, layer_goal])\n",
        "          final_layers = np.append(final_layers, layer, axis = 0)\n",
        "\n",
        "        return final_layers\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "x_Ub3gTejWFS"
      },
      "outputs": [],
      "source": [
        "m=n=5\n",
        "n_agents = 3\n",
        "entities = [10,4, 6]\n",
        "priority = [1,2,2]\n",
        "agents = [[4,0], [4,2], [4,4]]\n",
        "goals = [[0,4], [0,2], [0,0]]\n",
        "prioritylevels = [\"H\",\"E\",\"E\"]\n",
        "verbose = True\n",
        "env = GridEnvt( m, n, n_agents, agents, goals, entities, 200, priority, prioritylevels, 20, verbose)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nJ2er8UBjWFW",
        "outputId": "6c06cdd5-d2df-4351-ca7a-3bb2ec31d06d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 40  -1  60  -1 100]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ -1  -1 160  -1  -1]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ 90  -1  50  -1  30]]\n",
            "\n",
            "\n",
            "One-Hot Encoding\n",
            "[[[0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [1. 0. 0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 1. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0. 0. 1.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 1.]]\n",
            "\n",
            " [[0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 1. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]]\n",
            "\n",
            " [[1. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 1. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 1. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 1. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]\n",
            "  [0. 0. 0. 0. 0.]]]\n",
            "SHape: (9, 5, 5)\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "s = env.reset()\n",
        "print(s)\n",
        "env.render()\n",
        "one_hot = env.get_state_layers()\n",
        "print(f\"\\n\\nOne-Hot Encoding\")\n",
        "print(one_hot)\n",
        "print(f\"SHape: {one_hot.shape}\")\n",
        "env.goals_count\n",
        "env.render()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "sRmAB0bK-AI9"
      },
      "outputs": [],
      "source": [
        "class Agent():\n",
        "    def __init__(self, env):\n",
        "        self.env = env\n",
        "        self.observation_space = env.observation_space\n",
        "        self.action_space = env.action_space\n",
        "        self.epsilon = 0.5\n",
        "\n",
        "    def step(self):\n",
        "        return [np.random.choice(self.action_space.n),np.random.choice(self.action_space.n)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "IDdcYHcAjWFZ",
        "outputId": "8c99af66-10ee-486e-b71c-635488b6f69c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1, 2, 2], [1, 2, 2], [2, 0, 2], [2, 0, 2], [2, 0, 2], [1, 0, 1], [1, 2, 2], [2, 2, 2], [0, 0, 1]]\n",
            "[[ 60  -1  40  -1 100]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ -1  -1 160  -1  -1]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ 90  -1  30  -1  50]]\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Just checking if environment is working fine.\n",
        "m=n=5\n",
        "n_agents = 3\n",
        "agents = [[4,0], [4,4], [4,2]]\n",
        "goals = [[0,4], [0,0], [0,2]]\n",
        "entities = [10,6, 8]\n",
        "priority = [1,2,2]\n",
        "prioritylevels = [\"H\",\"M\",\"M\"]\n",
        "verbose = True\n",
        "env = GridEnvt( m, n, n_agents, agents, goals, entities, 200, priority, prioritylevels, 20, verbose)\n",
        "agent = Agent(env)\n",
        "\n",
        "obs = env.reset()\n",
        "done = False\n",
        "# actss = [[1,0,2], [1,0,2], [2,2,2], [2,2,2], [1,0,1], [1,0,2], [2,2,0], [2,0,1], [3,2,1]]\n",
        "actss = [[1, 1, 2, 2, 2, 1, 1, 2, 0], [2, 2, 0, 0, 0, 0, 2, 2, 0], [2, 2, 2, 2, 2, 1, 2, 2, 1]]\n",
        "# actss = [1, 1, 2, 2, 2, 1, 1, 2, 0]\n",
        "k=0\n",
        "rrr = [0]*3\n",
        "i=0\n",
        "acs = []\n",
        "for i in range(len(actss[0])):\n",
        "  m = []\n",
        "  for j in range(3):\n",
        "    m.append(actss[j][i])\n",
        "  acs.append(m)\n",
        "print(acs)\n",
        "print(env.grid)\n",
        "k = 0\n",
        "while not done:\n",
        "    \n",
        "    obs, reward, dones, info = env.step(actss)\n",
        "    k+=1\n",
        "    rrr = np.add(rrr, reward)\n",
        "    # actss.append(actss[k-1])\n",
        "    env.dones=dones\n",
        "    env.render()\n",
        "    for i in dones:\n",
        "        if i==False:\n",
        "            done = False\n",
        "        else:\n",
        "            done = True\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgxIXydvUG3h"
      },
      "source": [
        "**DQN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "GLQWvhdGUKKX"
      },
      "outputs": [],
      "source": [
        "from torch.nn.modules.loss import MSELoss\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "class dqn_Agent():\n",
        "  def __init__(self, env, alpha, gamma, epsilon, batch_size, max_memory, max_episodes, isDDQN=False):\n",
        "    self.env = env\n",
        "    self.observation_space = env.observation_space\n",
        "    self.action_space = env.action_space\n",
        "    self.alpha = alpha\n",
        "    self.gamma = gamma\n",
        "    self.epsilon = epsilon\n",
        "    self.max_episodes = max_episodes\n",
        "    self.train_batch_size = batch_size\n",
        "    self.replay_memory = [deque(maxlen=max_memory) for i in range(n_agents)]\n",
        "    self.model = []\n",
        "    self.target_model = []\n",
        "    self.optimizer = []\n",
        "    self.loss_function = []\n",
        "    self.isDDQN = isDDQN\n",
        "    self.stop_training = [False for i in range(env.n_agents)]\n",
        "    self.training_count = [0] * self.env.n_agents\n",
        "    \n",
        "    model_inp = 3 * env.n_agents * env.m * env.n\n",
        "    for i in range(self.env.n_agents):\n",
        "      self.model.append(torch.nn.Sequential( OrderedDict([('layer-1', torch.nn.Linear(model_inp, 175)), ('relu-1',torch.nn.ReLU()), \n",
        "                                                          ('layer-2',torch.nn.Linear(175, 100)), ('relu-2',torch.nn.ReLU()),\n",
        "                                                          ('layer-3',torch.nn.Linear(100, 75)), ('relu-2',torch.nn.ReLU()),\n",
        "                                                          ('last',torch.nn.Linear(75,4))])))\n",
        "      self.target_model.append(copy.deepcopy(self.model[i]))\n",
        "      self.target_model[i].load_state_dict(self.model[i].state_dict())\n",
        "      self.optimizer.append(torch.optim.Adam(self.model[i].parameters()))\n",
        "      self.loss_function.append(torch.nn.MSELoss())\n",
        "      \n",
        "  def choose_action(self, qval, is_test):\n",
        "      if (np.random.random() > self.epsilon) or (is_test):\n",
        "        act = np.argmax(qval.to('cpu').data.numpy())\n",
        "        return act\n",
        "      else:\n",
        "        return np.random.choice(self.action_space.n)\n",
        "\n",
        "  def epsilon_decay(self, time, episodes):\n",
        "      A=0.5 #to explore longer duration\n",
        "      B=0.3 #decides the slope of transition between exploration and exploitation\n",
        "      C=0.1 #controls the steepnes of left and right tails\n",
        "      standardized_time=(time-A*episodes)/(B*episodes)\n",
        "      cosh=np.cosh(math.exp(-standardized_time))\n",
        "      epsilon=1.1-(1/cosh+(time*C/episodes))\n",
        "      return epsilon\n",
        "  \n",
        "  def remember_transition(self, agent, s, a, r, next_st, done):\n",
        "      self.replay_memory[agent].append((s, a, r, next_st, done))\n",
        "  \n",
        "  def train_dqn(self):\n",
        "      if len(self.replay_memory[0])>=self.train_batch_size:\n",
        "        for agent in range(self.env.n_agents):\n",
        "          if not self.stop_training[agent]:\n",
        "              self.training_count[agent] += 1\n",
        "              states, actions, rewards, next_states, dones = zip(*random.sample(self.replay_memory[agent], self.train_batch_size))\n",
        "              states = torch.cat(states).to(device)\n",
        "              actions = torch.Tensor(actions).to(device)\n",
        "              rewards = torch.Tensor(rewards).to(device)\n",
        "              next_states = torch.cat(next_states).to(device)\n",
        "              dones = torch.Tensor(dones).to(device)\n",
        "\n",
        "              qval = self.model[agent](states)\n",
        "              X = qval.gather(1, actions.long().unsqueeze(1)).squeeze(1)\n",
        "\n",
        "              if self.isDDQN:\n",
        "                  th_t = self.model[agent](next_states)\n",
        "                  max_action = torch.argmax(th_t, dim=1)\n",
        "                  th_t_ = self.target_model[agent](next_states)                  \n",
        "                  Y = rewards + self.gamma * (1 - dones) *  th_t_.gather(1, max_action.long().unsqueeze(1)).squeeze(1)\n",
        "              else:\n",
        "                  next_qval = self.target_model[agent](next_states)\n",
        "                  Y = rewards + self.gamma * (1-dones)*torch.max(next_qval, dim=1)[0]\n",
        "\n",
        "              loss = self.loss_function[agent](X,Y)\n",
        "              self.optimizer[agent].zero_grad()\n",
        "              loss.backward()\n",
        "              self.optimizer[agent].step()\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "N9LSfrVour9I"
      },
      "outputs": [],
      "source": [
        "class DQN():\n",
        "  def __init__(self, env, alpha, gamma, epsilon, batch_size,update_target_for_every, replay_memory_size, max_timesteps, max_episodes, isDDQN):\n",
        "    self.env = env\n",
        "    self.env.max_timesteps = max_timesteps\n",
        "    self.alpha = alpha\n",
        "    self.gamma = gamma\n",
        "    self.epsilon = epsilon\n",
        "    self.batch_size = batch_size\n",
        "    self.replay_memory_size = replay_memory_size\n",
        "    self.max_episodes = max_episodes\n",
        "    self.isDDQN = isDDQN\n",
        "    self.dqnA = dqn_Agent(self.env, alpha, gamma, epsilon, batch_size, replay_memory_size, max_episodes,self.isDDQN)\n",
        "    step = 0\n",
        "    self.reached_at_timesteps = [[] for h in range(env.n_agents)]\n",
        "    self.total_rewards = [[] for h in range(env.n_agents)]\n",
        "    self.epsilons = []\n",
        "    self.actions_taken = [[] for h in range(env.n_agents)]\n",
        "    self.update_target_for_every = 30\n",
        "    self.isAllDone = False\n",
        "    self.total_agents_rewards_per_ep = []\n",
        "    self.dqnA.stop_training = [False for i in range(env.n_agents)]\n",
        "    self.agents_rew_per_ep = [[] for h in range(env.n_agents)]\n",
        "    \n",
        "\n",
        "  def play_dqn(self, isTest):\n",
        "    \n",
        "    step = 0\n",
        "    self.reached_at_timesteps = [[] for h in range(self.env.n_agents)]\n",
        "    self.total_rewards = [[] for h in range(self.env.n_agents)]\n",
        "    self.agents_rew_per_ep = [[] for h in range(env.n_agents)]\n",
        "    self.total_agents_rewards_per_ep = []\n",
        "    self.epsilons = []\n",
        "    self.update_target_for_every = 30\n",
        "    self.isAllDone = False\n",
        "    state_function = lambda env, curr_state: torch.from_numpy(env.get_state_layers().reshape(1,225) + np.random.random()).float().to(device)\n",
        "    for ep in range(self.max_episodes):\n",
        "        s = self.env.reset()\n",
        "        cur_st = state_function(self.env, s)\n",
        "        self.action_to_take = []\n",
        "        self.reward_per_ep = [0] * self.env.n_agents\n",
        "        self.reached_at_timestep = [-1]*self.env.n_agents\n",
        "        self.actions_taken = [[] for h in range(self.env.n_agents)]\n",
        "        k = 0\n",
        "        self.isAllDone = False\n",
        "        while not self.isAllDone:\n",
        "            action_to_take = []\n",
        "            for agent in range(env.n_agents):\n",
        "              cur_state_qvals = self.dqnA.model[agent](cur_st)\n",
        "              action_to_take.append(self.dqnA.choose_action(cur_state_qvals, isTest))\n",
        "              self.actions_taken[agent].append(action_to_take[agent])\n",
        "            ns_n, rew_n, dones_n, info_n = self.env.step(action_to_take)\n",
        "            self.reward_per_ep = np.add(self.reward_per_ep,rew_n)\n",
        "            next_state = state_function(env, ns_n)\n",
        "            if not isTest:\n",
        "              for agent in range(self.env.n_agents):\n",
        "                if not self.dqnA.stop_training[agent]:\n",
        "                    self.dqnA.remember_transition(agent, cur_st, action_to_take[agent], rew_n[agent], next_state, dones_n[agent])\n",
        "              \n",
        "              self.dqnA.train_dqn()\n",
        "\n",
        "            for i,val in enumerate(dones_n):\n",
        "              if val and (self.reached_at_timestep[i]==-1):\n",
        "                self.reached_at_timestep[i] = env.timestep\n",
        "\n",
        "            self.isAllDone = all(dones_n)\n",
        "            if self.isAllDone:\n",
        "              for i in range(self.env.n_agents):\n",
        "                if self.reached_at_timestep[i]==-1:\n",
        "                  self.reached_at_timestep[i] = self.env.timestep\n",
        "                  print(f\"is_agent_silent = {self.env.is_agent_silent}\")\n",
        "\n",
        "            s = ns_n\n",
        "            cur_st = next_state\n",
        "        k+=1\n",
        "        if k % self.update_target_for_every == 0:\n",
        "          for agent in range(self.env.n_agents):\n",
        "            self.dqnA.target_model[agent].load_state_dict(self.dqnA.model[agent].state_dict())\n",
        "        if not isTest:\n",
        "          self.dqnA.epsilon*=0.995\n",
        "          self.epsilons.append(self.dqnA.epsilon)\n",
        "        \n",
        "        for agent in range(self.env.n_agents):\n",
        "            self.agents_rew_per_ep[agent].append(self.reward_per_ep[agent])\n",
        "\n",
        "        for agent in range(self.env.n_agents):\n",
        "          self.total_rewards[agent].append(self.reward_per_ep[agent])\n",
        "          self.reached_at_timesteps[agent].append(self.reached_at_timestep[agent])\n",
        "        self.total_agents_rewards_per_ep.append(sum(self.reward_per_ep))\n",
        "\n",
        "        if ep%10 == 0:\n",
        "          if (sum(self.total_rewards[0][-10:])/10 >= 28) and (not self.dqnA.stop_training[0]):\n",
        "              self.dqnA.stop_training[0] = True\n",
        "          if (sum(self.total_rewards[1][-10:])/10 >= 21) and (not self.dqnA.stop_training[1]):\n",
        "              self.dqnA.stop_training[1] = True\n",
        "          if (sum(self.total_rewards[2][-10:])/10 >= 18) and (not self.dqnA.stop_training[2]):\n",
        "              self.dqnA.stop_training[2] = True\n",
        "          u = int(self.max_episodes/10)\n",
        "          if (ep%u) == 0:\n",
        "            print(f\"Episode: {ep} Last avg reward: {sum(self.total_agents_rewards_per_ep[-u:])/ u}   Episode Rewards: {self.reward_per_ep}   Training: {self.dqnA.stop_training}   is_agent_silent values: {self.env.is_agent_silent}\")\n",
        "      \n",
        "        if all(self.dqnA.stop_training):\n",
        "            print(\"Enough Training !!\")\n",
        "            break\n",
        "    print(f\"Total Rewards: {self.total_agents_rewards_per_ep}\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "Zg2s6l5kNFEB"
      },
      "outputs": [],
      "source": [
        "class visualization():\n",
        "  def __init__(self, algo):\n",
        "    self.algo = algo\n",
        "\n",
        "  def plot_graph(self,x, x_axis, y_axis, label_,doPlot=True):\n",
        "      plt.plot(x,label=label_)\n",
        "      plt.xlabel(x_axis)\n",
        "      plt.ylabel(y_axis)\n",
        "      plt.legend()\n",
        "      if doPlot:\n",
        "        plt.show()\n",
        "  def cumulative_rewards(self,algo):\n",
        "      y = [x for x in range(len(algo.total_rewards[0]))]\n",
        "      np.shape(y)\n",
        "      s = 0\n",
        "      cumu_rew = np.zeros([len(y)])\n",
        "      cumu_rew_1 = np.zeros([len(y)])\n",
        "      cumu_rew_2 = np.zeros([len(y)])\n",
        "      for i in range(len(algo.total_rewards[0])):\n",
        "          s+=algo.total_rewards[0][i]\n",
        "          cumu_rew[i] = s\n",
        "      s = 0\n",
        "      for i in range(len(algo.total_rewards[1])):\n",
        "          s+=algo.total_rewards[1][i]\n",
        "          cumu_rew_1[i] = s\n",
        "      s = 0\n",
        "      for i in range(len(algo.total_rewards[1])):\n",
        "          s+=algo.total_rewards[2][i]\n",
        "          cumu_rew_2[i] = s\n",
        "      plt.plot(cumu_rew, label=\"Agent 1\")\n",
        "      plt.plot(cumu_rew_1, label=\"Agent 2\")\n",
        "      plt.plot(cumu_rew_2, label=\"Agent 3\")\n",
        "      plt.xlabel('Episodes')\n",
        "      plt.ylabel('Cumulative Reward')\n",
        "      plt.title('Cumulative Reward per Episode')\n",
        "      plt.show()\n",
        "  \n",
        "  def averages(self,list_):\n",
        "      total_rewards = []\n",
        "      l = len(list_)\n",
        "      list_ = np.array(list_)\n",
        "      avgList = np.average(list_[l%10:].reshape(-1,10), axis=1)\n",
        "      print(len(avgList))\n",
        "      return avgList\n",
        "\n",
        "\n",
        "  def draw(self, isTest=False):\n",
        "      do_plot = True\n",
        "      plt.plot(figsize=(20,5))\n",
        "      if isTest:\n",
        "          do_plot=False\n",
        "      else:\n",
        "          self.plot_graph(self.algo.epsilons, \"Episodes\", \"Epsilons\", \"Epsilon Decay\")\n",
        "      self.plot_graph(self.algo.total_rewards[0], \"Episodes\", \"Agents Rewards\", \"Agent 1\",False)\n",
        "      self.plot_graph(self.algo.total_rewards[1], \"Episodes\", \"Agents Rewards\", \"Agent 2\",False)\n",
        "      self.plot_graph(self.algo.total_rewards[2], \"Episodes\", \"Agents Rewards\", \"Agent 3\")\n",
        "\n",
        "      self.plot_graph(self.algo.reached_at_timesteps[0], \"Episodes\", \"Agents Reached at\", \"Agent 1\", False)\n",
        "      self.plot_graph(self.algo.reached_at_timesteps[1], \"Episodes\", \"Agents Reached at\", \"Agent 2\", False)\n",
        "      self.plot_graph(self.algo.reached_at_timesteps[2], \"Episodes\", \"Agents Reached at\", \"Agent 3\")\n",
        "\n",
        "      self.plot_graph(self.algo.total_agents_rewards_per_ep, \"Episodes\", \"Total Rewards per episode\", \"All Agents\")\n",
        "\n",
        "      self.cumulative_rewards(dqn)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tjfn70W92lG"
      },
      "source": [
        "###**DDQN**###"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HD5pXEPYvTUV",
        "outputId": "cc55b4fc-d47a-45ac-a09c-dc8344198dfc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Episode: 0 Last avg reward: 0.006666666666666667   Episode Rewards: [-6 -4 12]   Training: [False, False, False]   is_agent_silent values: [False, False, False]\n",
            "Episode: 300 Last avg reward: 26.61   Episode Rewards: [26 12 10]   Training: [False, False, False]   is_agent_silent values: [True, False, False]\n",
            "Episode: 600 Last avg reward: 56.083333333333336   Episode Rewards: [24 19 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Episode: 900 Last avg reward: 63.973333333333336   Episode Rewards: [26 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Episode: 1200 Last avg reward: 61.96666666666667   Episode Rewards: [26 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Episode: 1500 Last avg reward: 60.263333333333335   Episode Rewards: [12 19 19]   Training: [False, True, True]   is_agent_silent values: [False, True, True]\n",
            "Episode: 1800 Last avg reward: 55.57666666666667   Episode Rewards: [12 17 19]   Training: [False, True, True]   is_agent_silent values: [False, True, True]\n",
            "Episode: 2100 Last avg reward: 58.556666666666665   Episode Rewards: [12 19 19]   Training: [False, True, True]   is_agent_silent values: [False, True, True]\n",
            "Episode: 2400 Last avg reward: 55.14333333333333   Episode Rewards: [25 22 20]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Episode: 2700 Last avg reward: 58.60666666666667   Episode Rewards: [26 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Total Rewards: [2, 8, -4, 2, 14, 6, -18, 4, -8, 12, -12, 2, -14, 8, 6, -18, 18, 10, 20, -2, -12, -2, -10, 10, 18, -2, 6, -10, 0, -14, 20, -20, 8, -4, -12, -14, -22, 18, 10, 2, -4, 26, 22, 14, 8, 8, 12, 20, 10, -8, 24, 10, 22, 18, 2, -14, 0, 32, -6, 34, 18, 14, 20, 22, 20, -16, -4, 52, 35, 22, 20, -2, 24, 22, 14, 12, 34, -8, 20, 22, 8, 23, 14, -2, 18, 36, 24, -6, 12, 38, 24, 20, 30, 16, -2, 32, 25, -10, 24, 10, 8, 6, 36, 20, 24, 22, 30, 28, 21, 15, 30, 30, 30, 10, 36, 36, 32, 18, 24, 16, 16, 32, 6, 22, 30, 12, 24, 32, 20, 14, 27, 10, 20, 44, 34, 18, 46, 34, 28, 6, 17, 15, 34, 44, 22, 40, 44, 19, 52, 28, 24, 15, 32, 49, 35, 32, 14, 26, 35, 34, 38, 25, 25, 4, 4, 46, 26, 38, 34, 30, 45, 6, 49, 6, 28, 48, 29, 20, 50, 41, 50, 29, 36, 49, 32, 58, 10, 39, 50, 44, 35, -18, 49, 39, 18, 44, 49, 41, 42, 38, 29, 56, 36, 30, 33, 37, 24, 37, 49, 10, 45, 32, 26, 26, 46, 36, 30, 41, 46, 31, 24, 49, 36, 34, 30, 48, 31, 37, 8, 26, 52, 57, 31, 40, 49, 36, 49, 56, 34, 48, 61, 47, 39, 33, 35, 50, 49, 53, 36, 44, 52, 63, 38, 51, 36, 32, 54, 37, 50, 54, 48, 64, 2, 58, 32, 43, 32, 43, 18, 39, 53, 36, 45, 30, 38, 46, 62, 42, 62, 50, 30, 42, 30, 28, 57, 42, 51, 48, 55, 54, 64, 36, 36, 56, 26, 52, 45, 46, 47, 65, 48, 62, 55, 58, 64, 57, 62, 46, 14, 30, 59, 55, 16, 50, 53, 54, 27, 61, 49, 50, 65, 59, 48, 59, 61, 54, 34, 55, 52, 59, 37, 57, 53, 49, 30, 40, 39, 25, 44, 47, 27, 57, 62, 48, 39, 52, 66, 56, 39, 47, 48, 62, 49, 54, 58, 68, 59, 34, 64, 46, 44, 61, 59, 41, 58, 59, 44, 38, 45, 52, 64, 66, 58, 45, 30, 46, 32, 61, 60, 40, 43, 66, 34, 56, 56, 67, 34, 56, 66, 44, 40, 34, 64, 66, 61, 55, 63, 66, 52, 62, 59, 57, 53, 63, 64, 61, 28, 67, 61, 60, 67, 52, 56, 59, 59, 62, 31, 43, 48, 68, 64, 67, 60, 38, 64, 41, 48, 53, 66, 66, 62, 58, 68, 65, 62, 56, 55, 68, 64, 59, 59, 65, 66, 66, 31, 62, 63, 61, 45, 53, 66, 39, 62, 40, 63, 66, 65, 66, 48, 64, 56, 61, 45, 30, 65, 63, 65, 63, 38, 62, 66, 66, 66, 63, 64, 50, 44, 62, 66, 57, 59, 49, 58, 51, 59, 64, 63, 52, 66, 43, 62, 32, 38, 39, 64, 33, 41, 59, 58, 66, 66, 52, 66, 66, 66, 61, 59, 66, 66, 59, 65, 42, 66, 59, 66, 66, 36, 43, 64, 68, 62, 66, 57, 55, 40, 57, 68, 63, 61, 66, 39, 59, 63, 59, 68, 61, 66, 66, 65, 63, 66, 56, 58, 66, 66, 68, 68, 61, 68, 66, 59, 68, 66, 43, 66, 66, 36, 68, 64, 62, 66, 57, 66, 42, 59, 47, 68, 68, 55, 65, 63, 64, 55, 54, 64, 62, 42, 49, 66, 56, 33, 62, 66, 66, 62, 62, 67, 66, 68, 68, 68, 66, 66, 66, 66, 68, 60, 64, 66, 66, 62, 46, 66, 62, 66, 40, 66, 66, 61, 66, 68, 59, 66, 64, 67, 68, 65, 66, 65, 66, 66, 68, 66, 60, 38, 43, 66, 66, 60, 60, 62, 66, 66, 68, 65, 66, 66, 64, 66, 66, 64, 66, 62, 66, 66, 64, 53, 54, 66, 66, 66, 63, 66, 66, 66, 64, 61, 65, 66, 65, 66, 63, 66, 68, 66, 66, 63, 66, 66, 53, 66, 65, 66, 66, 59, 66, 66, 54, 62, 66, 66, 59, 66, 66, 64, 65, 65, 66, 59, 61, 66, 35, 68, 61, 68, 66, 65, 66, 63, 66, 68, 62, 61, 66, 68, 66, 52, 66, 66, 66, 66, 68, 66, 66, 68, 66, 66, 46, 66, 64, 62, 66, 38, 64, 66, 67, 59, 66, 66, 66, 66, 66, 66, 65, 63, 68, 68, 66, 66, 66, 64, 66, 66, 66, 66, 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50, 48, 50, 68, 50]\n",
            "[[ 30  90  50  -1 100]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ -1  -1 160  -1  -1]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ -1  -1  -1  -1  -1]]\n",
            "[[2, 1, 1, 2, 0, 2, 2, 2, 2, 2], [2, 2, 0, 0, 0, 2, 0, 0, 2, 0], [2, 2, 2, 2, 2, 3, 3, 1, 1, 1]]\n"
          ]
        }
      ],
      "source": [
        "m=n=5\n",
        "n_agents = 3\n",
        "agents = [[4,0], [4,2], [4,4]]\n",
        "goals = [[0,4], [0,2], [0,0]]\n",
        "entities = [10,6, 8]\n",
        "resource_capacity = 180\n",
        "priority = [1,2,2]\n",
        "prioritylevels = [\"H\",\"E\",\"E\"]\n",
        "verbose = False\n",
        "env = GridEnvt( m, n, n_agents, agents, goals, entities, resource_capacity, priority, prioritylevels, 20, verbose)\n",
        "# env = GridEnvt(5, 5, 3, [10,6,4], [1,2,3], 20, False)\n",
        "dqn = DQN(env, alpha=0.001, gamma=0.99, epsilon=1.0, batch_size=128, replay_memory_size=3000, update_target_for_every=30, max_timesteps=10, max_episodes=3000, isDDQN = True)\n",
        "dqn.play_dqn(False)\n",
        "print(env.grid)\n",
        "print(dqn.actions_taken)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "AP5wFahrGr8C",
        "outputId": "170ab857-33cb-4064-f70f-44f598c61fb2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x144 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "300\n",
            "300\n",
            "300\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x144 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "def averages(list_):\n",
        "      total_rewards = []\n",
        "      l = len(list_)\n",
        "      list_ = np.array(list_)\n",
        "      avgList = np.average(list_[l%10:].reshape(-1,10), axis=1)\n",
        "      print(len(avgList))\n",
        "      return avgList\n",
        "\n",
        "plt.figure(figsize=(15,2))\n",
        "plt.plot(dqn.total_rewards[0], color='r', label='Agent 1')\n",
        "plt.plot(dqn.total_rewards[1], color='c', label='Agent 2')\n",
        "plt.plot(dqn.total_rewards[2], color='yellow', label='Agent 3')\n",
        "plt.xlabel(\"Episodes\")\n",
        "plt.ylabel(\"Rewards\")\n",
        "plt.title(\"DDQN\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "  \n",
        "plt.figure(figsize=(12,2))\n",
        "plt.plot(averages(dqn.total_rewards[0]), color='r', label='Agent 1')\n",
        "plt.plot(averages(dqn.total_rewards[1]), color='c', label='Agent 2')\n",
        "plt.plot(averages(dqn.total_rewards[2]), color='yellow', label='Agent 3')\n",
        "plt.xlabel(\"Episodes\")\n",
        "plt.ylabel(\"Average Rewards\")\n",
        "plt.title(\"DDQN\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "7DZmzSG98oRs"
      },
      "outputs": [],
      "source": [
        "# torch.save(dqn.dqnA.model[0].state_dict(), 'dqn_agent0_may10_v2.pt')\n",
        "# torch.save(dqn.dqnA.model[1].state_dict(), 'dqn_agent1_may10_v2.pt')\n",
        "# torch.save(dqn.dqnA.model[2].state_dict(), 'dqn_agent2_may10_v2.pt')\n",
        "\n",
        "# torch.save(dqn.dqnA.model[0].state_dict(), 'ddqn_agent0_v5.pt')\n",
        "# torch.save(dqn.dqnA.model[1].state_dict(), 'ddqn_agent1_v5.pt')\n",
        "# torch.save(dqn.dqnA.model[2].state_dict(), 'ddqn_agent2_v5.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_iwarnIoC1I",
        "outputId": "6cac153f-137e-49e5-8f0b-319c522e4c63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#DDQN Evaluation \n",
        "dqn.model = []\n",
        "model_inp = 3 * env.n_agents * env.m * env.n\n",
        "for i in range(dqn.env.n_agents):\n",
        "  dqn.dqnA.model.append(torch.nn.Sequential( OrderedDict([('layer-1', torch.nn.Linear(model_inp, 175)), ('relu-1',torch.nn.ReLU()), \n",
        "                                                          ('layer-2',torch.nn.Linear(175, 100)), ('relu-2',torch.nn.ReLU()),\n",
        "                                                          ('layer-3',torch.nn.Linear(100, 75)), ('relu-2',torch.nn.ReLU()),\n",
        "                                                          ('last',torch.nn.Linear(75,4))])))\n",
        "dqn.dqnA.model[0].load_state_dict(torch.load('ddqn_agent0_v5.pt'))\n",
        "dqn.dqnA.model[1].load_state_dict(torch.load('ddqn_agent1_v5.pt'))\n",
        "dqn.dqnA.model[2].load_state_dict(torch.load('ddqn_agent2_v5.pt'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R4CCkZVZ-NFz",
        "outputId": "37d9d188-8b47-45d7-b072-77205e614038"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Episode: 0 Last avg reward: 13.2   Episode Rewards: [26 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Episode: 10 Last avg reward: 62.0   Episode Rewards: [26 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Episode: 20 Last avg reward: 65.6   Episode Rewards: [24 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Episode: 30 Last avg reward: 66.4   Episode Rewards: [26 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Episode: 40 Last avg reward: 65.6   Episode Rewards: [26 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Total Rewards: [66, 66, 66, 66, 66, 66, 66, 66, 66, 46, 66, 44, 62, 66, 66, 66, 66, 66, 66, 66, 64, 64, 66, 66, 66, 66, 66, 66, 66, 68, 66, 66, 61, 66, 66, 66, 66, 66, 64, 66, 66, 68, 66, 66, 64, 66, 66, 64, 66, 41]\n",
            "[0, 0, 1]\n",
            "[[1, 1, 2, 2, 0, 2, 2, 2, 2, 2], [0, 2, 2, 0, 0, 2, 0, 2, 2, 2], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]]\n",
            "[[26, 26, 26, 26, 26, 26, 26, 26, 26, 28, 26, 26, 24, 26, 26, 26, 26, 26, 26, 26, 24, 26, 26, 26, 26, 26, 26, 26, 26, 28, 26, 26, 26, 26, 26, 26, 26, 26, 24, 26, 26, 28, 26, 26, 24, 26, 26, 24, 26, 12], [21, 21, 21, 21, 21, 21, 21, 21, 21, 0, 21, 0, 19, 21, 21, 21, 21, 21, 21, 21, 21, 19, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 16, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 10], [19, 19, 19, 19, 19, 19, 19, 19, 19, 18, 19, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19]]\n",
            "Rewards per ep: [12 10 19]\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dqn.max_episodes=50\n",
        "dqn.play_dqn(True)\n",
        "print(env.goals_count)\n",
        "print(dqn.actions_taken)\n",
        "print(dqn.total_rewards)\n",
        "print(f\"Rewards per ep: {dqn.reward_per_ep}\")\n",
        "animate = visualization(dqn)\n",
        "animate.draw(True)\n",
        "# for ep in range(dqn.max_episodes):\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWIqLFhU0MzY",
        "outputId": "8aa74d08-5200-4303-b816-ea66978d2bbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 10, 8, 10, 8, 8, 8, 10, 8, 8, 8, 8, 8, 10, 8, 8, 8, 8, 8, 8, 8, 8, 10, 10, 8, 8, 8, 8, 10, 8, 10, 8, 8, 8, 8, 8, 8, 8, 8, 8, 10], [9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 9, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10], [5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]]\n"
          ]
        }
      ],
      "source": [
        "print(dqn.reached_at_timesteps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XsDemZhFvj8"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEuO711HdUvc"
      },
      "source": [
        "##**DQN**##"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GV8s3nXt9HeL",
        "outputId": "c3369f26-9064-4f54-ba01-2e540f78cf5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Episode: 0 Last avg reward: -0.05333333333333334   Episode Rewards: [-4 -6 -6]   Training: [False, False, False]   is_agent_silent values: [False, False, False]\n",
            "Episode: 300 Last avg reward: 26.37   Episode Rewards: [12 18 18]   Training: [False, False, False]   is_agent_silent values: [False, True, True]\n",
            "Episode: 600 Last avg reward: 56.28666666666667   Episode Rewards: [24 16 19]   Training: [False, True, True]   is_agent_silent values: [True, False, True]\n",
            "Episode: 900 Last avg reward: 64.94666666666667   Episode Rewards: [28 21 19]   Training: [False, True, True]   is_agent_silent values: [True, True, True]\n",
            "Enough Training !!\n",
            "Total Rewards: [-16, 22, -18, -20, -6, 10, -14, -6, -8, -12, -12, -6, -2, 18, -24, -10, 8, 2, -12, -14, 6, 0, -6, 20, 10, -4, 22, 8, -8, 2, -8, 32, 14, 20, -22, 10, -4, 2, 4, 2, -4, 20, -20, -10, 22, -4, 28, 6, 20, 0, 6, 18, 12, 22, -8, -12, 26, -18, 22, 6, 16, 4, 36, 8, 12, 12, 2, 22, 20, 31, 28, 28, -2, 30, 10, 42, 16, 44, 34, 27, 16, 8, 14, 28, 12, 27, 6, 14, -2, 26, 27, 33, 14, -6, 16, -4, 30, 16, 24, 10, 22, 10, 30, 28, -14, 0, 26, 22, 19, 28, 46, 22, 16, 6, 28, 28, 14, 30, 6, 31, 35, 16, 35, 14, 40, 16, 22, 33, 18, 33, 28, 26, 32, 41, 18, 28, 16, 30, 18, 8, 41, 16, 30, 14, 37, 12, 27, 28, 30, 30, 30, 35, 38, 24, 20, 32, 32, 36, 28, 34, 26, 30, 12, 16, 20, 42, -6, 41, 24, 18, 14, 34, 44, 49, 45, 41, 34, 40, 40, 42, 22, 17, 12, 14, 34, 57, 39, 40, 38, 22, 29, 48, 24, 30, 30, 30, 36, 33, 24, 58, 43, 45, 43, 46, 43, 44, 42, 46, 40, 14, 26, 27, 16, 30, 49, 31, 35, 33, 59, 36, 55, 28, 38, 43, 56, 48, 34, 28, 45, 36, 44, 40, 35, 45, 43, 55, 28, 38, 31, 42, 34, 39, 30, 57, 56, 42, 50, 47, 41, 45, 41, 43, 42, 59, 44, 47, 58, 56, 46, 34, 48, 36, 54, 41, 39, 52, 28, 44, 46, 31, 33, 53, 44, 61, 55, 39, 41, 32, 53, 33, 52, 42, 46, 56, 59, 50, 38, 55, 41, 43, 59, 53, 60, 54, 60, 16, 57, 24, 33, 52, 48, 58, 26, 58, 32, 59, 65, 38, 58, 51, 63, 52, 38, 23, 50, 46, 49, 60, 53, 39, 48, 62, 47, 64, 64, 59, 28, 60, 61, 57, 59, 61, 42, 65, 52, 46, 63, 58, 63, 58, 47, 53, 32, 51, 41, 38, 12, 62, 61, 50, 68, 59, 48, 63, 65, 32, 65, 21, 66, 55, 59, 52, 55, 63, 56, 62, 42, 51, 53, 63, 45, 47, 66, 66, 61, 52, 56, 52, 40, 37, 14, 47, 63, 38, 68, 64, 57, 52, 61, 65, 62, 29, 44, 60, 58, 12, 56, 39, 59, 43, 38, 66, 64, 66, 57, 50, 57, 46, 63, 61, 53, 58, 31, 45, 53, 58, 52, 68, 65, 41, 44, 57, 41, 65, 59, 41, 56, 61, 59, 68, 36, 62, 59, 43, 43, 66, 61, 62, 62, 66, 63, 56, 68, 48, 68, 49, 64, 43, 64, 62, 55, 41, 66, 68, 68, 63, 61, 59, 66, 50, 30, 28, 44, 64, 41, 66, 57, 66, 36, 64, 56, 41, 61, 66, 38, 66, 63, 68, 57, 66, 66, 66, 46, 64, 61, 64, 64, 46, 60, 66, 48, 61, 66, 58, 66, 66, 64, 66, 51, 57, 59, 68, 54, 68, 65, 64, 59, 66, 61, 41, 66, 50, 57, 64, 66, 66, 66, 64, 66, 52, 55, 65, 66, 61, 63, 68, 64, 66, 60, 31, 63, 64, 68, 66, 50, 56, 65, 66, 38, 68, 65, 65, 40, 38, 65, 65, 68, 68, 67, 65, 50, 66, 66, 45, 68, 55, 66, 60, 66, 52, 66, 64, 68, 64, 64, 68, 58, 68, 68, 65, 48, 50, 68, 58, 66, 64, 44, 64, 65, 66, 64, 63, 61, 36, 64, 68, 68, 64, 65, 68, 64, 64, 56, 52, 54, 47, 65, 64, 68, 39, 59, 61, 58, 68, 64, 64, 63, 55, 68, 68, 66, 66, 46, 68, 63, 68, 66, 68, 68, 64, 61, 68, 66, 64, 68, 68, 68, 68, 66, 68, 68, 68, 66, 64, 60, 65, 68, 40, 65, 68, 63, 66, 68, 63, 68, 40, 65, 63, 68, 68, 63, 60, 64, 66, 66, 66, 44, 64, 68, 63, 40, 66, 66, 66, 64, 66, 63, 66, 64, 68, 68, 68, 34, 64, 68, 62, 68, 68, 68, 66, 68, 64, 64, 66, 66, 68, 63, 66, 66, 66, 66, 68, 66, 66, 64, 45, 68, 64, 62, 64, 66, 65, 68, 65, 68, 66, 66, 66, 66, 64, 66, 68, 41, 66, 65, 64, 64, 59, 66, 68, 68, 68, 66, 68, 64, 66, 66, 57, 68, 68, 64, 66, 68, 66, 66, 66, 68, 66, 63, 68, 61, 68, 66, 64, 68, 68, 59, 64, 64, 68, 64, 67, 66, 64, 64, 61, 65, 68, 68, 68, 66, 68, 64, 60, 66, 64, 68, 68, 68, 68, 64, 59, 64, 64, 64, 64, 66, 66, 68, 66, 57, 47, 68, 52, 66, 64, 63, 68, 68, 68, 66, 64, 66, 65, 65, 68, 68, 64, 66, 68, 65, 64, 68, 66, 66, 66, 68, 68, 68, 68, 66, 64, 68, 66, 68, 68, 68, 68, 68, 59, 68, 66, 68, 66, 68, 66, 68, 68, 68, 68, 64, 68, 65, 66, 68, 43, 66, 40, 68, 68, 64, 64, 66, 66, 66, 68, 66, 68, 68, 68, 68, 66, 61, 66, 64, 68, 68, 68, 66, 66, 68, 68, 68, 66, 64, 66, 68, 68, 63, 68, 68, 66, 66, 67, 68, 66, 66, 64, 68, 66, 68, 64, 66, 66, 66, 64, 64, 66, 66, 64, 68, 68, 68, 68, 68, 66, 66, 68, 66, 63, 68, 68, 68, 68, 66, 62, 44, 66, 68, 67, 68, 68, 68, 65, 68, 68, 61, 66, 68, 68, 68, 68, 68, 68, 66, 66, 66, 68, 66, 64, 60, 68, 68, 66, 61, 66, 66, 66, 68, 66, 66, 66, 68, 66, 68, 64, 63, 68, 68, 68, 64, 64, 68, 68, 65, 44, 64, 68, 68, 68, 66, 68, 68, 68, 66, 66, 66, 68, 68, 68, 64, 66, 66, 68, 68, 66, 66, 64, 66, 66, 64, 68, 64, 68, 66, 68, 68, 68, 66, 66, 64, 66, 68, 68, 64, 66, 68, 66, 66, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68]\n",
            "[[ 30  -1  50  -1  90]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ -1  -1 160  -1  -1]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ -1  -1  -1  -1  -1]]\n",
            "[[2, 2, 1, 1, 1, 2, 3, 1, 2, 2], [0, 0, 2, 2, 2, 0, 2, 2, 0, 3], [2, 2, 2, 2, 2, 2, 1, 2, 2, 2]]\n"
          ]
        }
      ],
      "source": [
        "#DQN Training\n",
        "\n",
        "m=n=5\n",
        "n_agents = 3\n",
        "agents = [[4,0], [4,2], [4,4]]\n",
        "goals = [[0,4], [0,2], [0,0]]\n",
        "entities = [10,6, 8]\n",
        "resource_capacity = 180\n",
        "priority = [1,2,2]\n",
        "prioritylevels = [\"H\",\"E\",\"E\"]\n",
        "verbose = False\n",
        "env = GridEnvt( m, n, n_agents, agents, goals, entities, resource_capacity, priority, prioritylevels, 20, verbose)\n",
        "# env = GridEnvt(5, 5, 3, [10,6,4], [1,2,3], 20, False)\n",
        "dqn = DQN(env, alpha=0.001, gamma=0.99, epsilon=1.0, batch_size=128, replay_memory_size=3000, update_target_for_every=30, max_timesteps=10, max_episodes=3000, isDDQN = False)\n",
        "dqn.play_dqn(False)\n",
        "print(env.grid)\n",
        "print(dqn.actions_taken)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "TWENOq1M9NsU",
        "outputId": "c0c29821-02bc-4c1b-84fb-2aa6d080262a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x144 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "101\n",
            "101\n",
            "101\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x144 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "def averages(list_):\n",
        "      total_rewards = []\n",
        "      l = len(list_)\n",
        "      list_ = np.array(list_)\n",
        "      avgList = np.average(list_[l%10:].reshape(-1,10), axis=1)\n",
        "      print(len(avgList))\n",
        "      return avgList\n",
        "\n",
        "plt.figure(figsize=(15,2))\n",
        "plt.plot(dqn.total_rewards[0], color='r', label='Agent 1')\n",
        "plt.plot(dqn.total_rewards[1], color='c', label='Agent 2')\n",
        "plt.plot(dqn.total_rewards[2], color='yellow', label='Agent 3')\n",
        "plt.xlabel(\"Episodes\")\n",
        "plt.ylabel(\"Rewards\")\n",
        "plt.title(\"DQN\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "  \n",
        "plt.figure(figsize=(12,2))\n",
        "plt.plot(averages(dqn.total_rewards[0]), color='r', label='Agent 1')\n",
        "plt.plot(averages(dqn.total_rewards[1]), color='c', label='Agent 2')\n",
        "plt.plot(averages(dqn.total_rewards[2]), color='yellow', label='Agent 3')\n",
        "plt.xlabel(\"Episodes\")\n",
        "plt.ylabel(\"Average Rewards\")\n",
        "plt.title(\"DQN\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwL5hFJ59Sue",
        "outputId": "e4934314-1d05-4432-eb37-4043d660dc76"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#DQN Evaluation\n",
        "dqn.model = []\n",
        "model_inp = 3 * env.n_agents * env.m * env.n\n",
        "for i in range(dqn.env.n_agents):\n",
        "  dqn.dqnA.model.append(torch.nn.Sequential( OrderedDict([('layer-1', torch.nn.Linear(model_inp, 175)), ('relu-1',torch.nn.ReLU()), \n",
        "                                                          ('layer-2',torch.nn.Linear(175, 100)), ('relu-2',torch.nn.ReLU()),\n",
        "                                                          ('layer-3',torch.nn.Linear(100, 75)), ('relu-2',torch.nn.ReLU()),\n",
        "                                                          ('last',torch.nn.Linear(75,4))])))\n",
        "dqn.dqnA.model[0].load_state_dict(torch.load('dqn_agent0_may10_v2.pt'))\n",
        "dqn.dqnA.model[1].load_state_dict(torch.load('dqn_agent1_may10_v2.pt'))\n",
        "dqn.dqnA.model[2].load_state_dict(torch.load('dqn_agent2_may10_v2.pt'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "UncRyl0JBUBA"
      },
      "outputs": [],
      "source": [
        "def play_dqn_test(dqn, isTest):\n",
        "    \n",
        "    step = 0\n",
        "    reached_at_timesteps = [[] for h in range(dqn.env.n_agents)]\n",
        "    total_rewards = [[] for h in range(dqn.env.n_agents)]\n",
        "    agents_rew_per_ep = [[] for h in range(dqn.env.n_agents)]\n",
        "    total_agents_rewards_per_ep = []\n",
        "    isAllDone = False\n",
        "    state_function = lambda env, curr_state: torch.from_numpy(dqn.env.get_state_layers().reshape(1,225) + np.random.random()).float().to(device)\n",
        "    for ep in range(dqn.max_episodes):\n",
        "        s = dqn.env.reset()\n",
        "        cur_st = state_function(dqn.env, s)\n",
        "        actions_to_take = []\n",
        "        reward_per_ep = [0] * dqn.env.n_agents\n",
        "        reached_at_timestep = [-1]*dqn.env.n_agents\n",
        "        actions_taken = [[] for h in range(dqn.env.n_agents)]\n",
        "        k = 0\n",
        "        isAllDone = False\n",
        "        while not isAllDone:\n",
        "            action_to_take = []\n",
        "            for agent in range(dqn.env.n_agents):\n",
        "              cur_state_qvals = dqn.dqnA.model[agent](cur_st)\n",
        "              action_to_take.append(dqn.dqnA.choose_action(cur_state_qvals, isTest))\n",
        "              actions_taken[agent].append(action_to_take[agent])\n",
        "            ns_n, rew_n, dones_n, info_n = dqn.env.step(action_to_take)\n",
        "            reward_per_ep = np.add(reward_per_ep,rew_n)\n",
        "            next_state = state_function(dqn.env, ns_n)\n",
        "    \n",
        "            for i,val in enumerate(dones_n):\n",
        "              if val and (reached_at_timestep[i]==-1):\n",
        "                reached_at_timestep[i] = env.timestep\n",
        "\n",
        "            isAllDone = all(dones_n)\n",
        "            if isAllDone:\n",
        "              for i in range(dqn.env.n_agents):\n",
        "                if reached_at_timestep[i]==-1:\n",
        "                  reached_at_timestep[i] = dqn.env.timestep\n",
        "                  print(f\"is_agent_silent = {dqn.env.is_agent_silent}\")\n",
        "\n",
        "            s = ns_n\n",
        "            cur_st = next_state\n",
        "        k+=1\n",
        "                \n",
        "        for agent in range(dqn.env.n_agents):\n",
        "            agents_rew_per_ep[agent].append(reward_per_ep[agent])\n",
        "\n",
        "        for agent in range(dqn.env.n_agents):\n",
        "          total_rewards[agent].append(reward_per_ep[agent])\n",
        "          reached_at_timesteps[agent].append(reached_at_timestep[agent])\n",
        "        total_agents_rewards_per_ep.append(sum(reward_per_ep))\n",
        "\n",
        "        # print(f\"Rewards {ep}= {self.reward_per_ep}, \\t is_agent_silent values: {self.env.is_agent_silent}\")\n",
        "\n",
        "    print(f\"Total Rewards: {total_agents_rewards_per_ep}\")\n",
        "    plotss(total_rewards, \"Episodes\", \"Rewards\", DQN)\n",
        "    plotss(reached_at_timesteps, \"Episodes\", \"Reached at Timesteps\", DQN)\n",
        "    \n",
        "\n",
        "def plotss(x, xname, yname, title):\n",
        "    plt.figure(figsize=(15,2))\n",
        "    plt.plot(x[0], color='r', label='Agent 1')\n",
        "    plt.plot(x[1], color='c', label='Agent 2')\n",
        "    plt.plot(x[2], color='yellow', label='Agent 3')\n",
        "    plt.xlabel(xname)\n",
        "    plt.ylabel(yname)\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "xGGdSn6IDrpH",
        "outputId": "74b75e34-ce11-4629-d4ca-dda54c4563c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total Rewards: [68, 68, 13, 64, 68, 68, 68, 68, 64, 68, 53, 64, 62, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 63, 68, 68, 41, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x144 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x144 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1, 1, 1]\n",
            "[[2, 2, 1, 1, 1, 2, 3, 1, 2, 2], [0, 0, 2, 2, 2, 0, 2, 2, 0, 3], [2, 2, 2, 2, 2, 2, 1, 2, 2, 2]]\n",
            "[[-4, 14, -4, -8, -4, 0, -4, -4, -6, -6, -4, 0, -6, -4, -8, -2, 16, -4, 0, -2, 10, -6, 0, 14, 8, -2, 14, -2, -2, -4, -2, 8, -8, 14, -6, 0, -4, 10, -2, -6, -2, 12, -8, -8, 14, 0, 0, -4, 2, -4, 4, 12, 10, -6, -6, -2, 8, 2, 0, 0, 10, -2, 16, 2, -6, -2, 12, -2, 6, 0, 2, -4, 0, 2, 4, 10, -4, 14, 14, 12, 10, 12, 2, 12, 14, -2, 12, 0, -4, 14, 10, 16, 10, 12, 8, 0, 0, 12, 10, 6, 0, 8, 16, 10, -8, -2, 10, 12, 8, 10, 16, 12, 8, -4, 28, 0, 10, 2, 10, 12, 8, 10, 2, -2, 12, 12, 14, 0, -2, 2, -4, 12, 12, 12, 6, 12, 10, 8, 8, 14, 8, 10, 12, 2, -2, -2, 0, 12, -6, 12, 14, 10, 6, 12, 12, 10, 2, 14, 0, 12, -4, 14, 10, 0, 10, 12, -2, 21, 8, 14, 8, 12, 12, 26, 12, 10, 10, 26, 14, 8, 8, 0, 14, 0, 14, 24, 12, 16, 4, 10, 14, 28, -4, 8, 12, 0, 6, 0, -4, 26, 14, 28, 10, 26, 12, 12, 14, 16, 10, -4, 8, 12, 12, 12, 26, 14, 6, 14, 26, 4, 24, 0, 6, 12, 26, 10, 22, 0, 12, 12, 12, 26, 16, 12, 12, 26, -4, 12, 12, 26, 16, 14, -2, 26, 26, 12, 16, 12, 12, 12, 10, 26, 14, 28, 14, 12, 26, 24, 14, 14, 14, 28, 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19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 18, 18, 19, 19, 19, 19, 19, 19, 18, 18, 19, 19, 19, 19, 19, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 18, 19, 19, 19, 19, 18, 19, 19, 19, 15, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 18, 19, 19, 19, 19, 19, 17, 18, 19, 19, 19, 19, 19, 19, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 18, 19, 19, 19, 19, 19, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19]]\n",
            "Rewards per ep: []\n"
          ]
        }
      ],
      "source": [
        "dqn.max_episodes=50\n",
        "dqn.reward_per_ep = []\n",
        "# dqn.play_dqn(True)\n",
        "play_dqn_test(dqn, True)\n",
        "print(env.goals_count)\n",
        "print(dqn.actions_taken)\n",
        "print(dqn.total_rewards)\n",
        "print(f\"Rewards per ep: {dqn.reward_per_ep}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RIoJuuGwGNyF"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78gx4LAcjWFc"
      },
      "source": [
        "**Q-Learning**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "NMfobNzZjWFh"
      },
      "outputs": [],
      "source": [
        "def get_agent_pos_old(pos):\n",
        "    n = 5\n",
        "    return (n*pos[0])+pos[1]\n",
        "\n",
        "def get_agent_pos(pos,is_res_collected,size):\n",
        "    n = size\n",
        "    state = (n*pos[0])+pos[1]\n",
        "    if is_res_collected:\n",
        "      state = (state+25)%50\n",
        "    return state\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "LyOFrdlRjWFj"
      },
      "outputs": [],
      "source": [
        "class q_learning_Agent():\n",
        "    def __init__(self, env):\n",
        "        self.env = env\n",
        "        \n",
        "        self.observation_space = env.observation_space\n",
        "        self.action_space = env.action_space\n",
        "        self.q_table = np.zeros([env.observation_space.n, env.action_space.n])\n",
        "\n",
        "    def step(self, env,epsilon,  agent, q_table, istest):\n",
        "      if (np.random.random() > epsilon) or (istest):\n",
        "        return np.argmax(q_table[get_agent_pos(env.agent_pos[agent], self.env.resource_collected[agent], self.env.m)])\n",
        "      else:\n",
        "        return np.random.choice(self.action_space.n)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "7GZJB2p2jWFl"
      },
      "outputs": [],
      "source": [
        "class play_q_learning():\n",
        "    def __init__(self,env, alpha, gamma, epsilon, max_eps):\n",
        "        self.env = env\n",
        "        self.epsilon = epsilon\n",
        "        self.alpha = alpha\n",
        "        self.gamma = gamma\n",
        "        self.max_episodes = max_eps\n",
        "\n",
        "        self.epsilons = []\n",
        "        self.rewards_per_eps = [[] for h in range(self.env.n_agents)]\n",
        "        self.dones_ = []\n",
        "        self.acts = {0:\"Left\", 1: \"Right\", 2:\"Up\", 3:\"Down\"}\n",
        "        self.eps = 0\n",
        "        self.actions_to_take = []\n",
        "        self.actions_taken = [[] for h in range(self.env.n_agents)]\n",
        "        self.reached_at_timestep = [[] for h in range(self.env.n_agents)]\n",
        "        self.q_tables = []\n",
        "        for i in range(self.env.n_agents):\n",
        "            self.q_tables.append(np.zeros([self.env.observation_space.n*2, self.env.action_space.n]))\n",
        "\n",
        "        self.q_agent = q_learning_Agent(env)\n",
        "    \n",
        "    def reset(self, is_test):\n",
        "        self.env = env\n",
        "\n",
        "        self.epsilons = []\n",
        "        self.rewards_per_eps = [[] for h in range(self.env.n_agents)]\n",
        "        self.dones_ = []\n",
        "        self.acts = {0:\"Left\", 1: \"Right\", 2:\"Up\", 3:\"Down\"}\n",
        "        self.eps = 0\n",
        "        self.actions_to_take = []\n",
        "        self.actions_taken = [[] for h in range(self.env.n_agents)]\n",
        "        self.reached_at_timestep = [[] for h in range(self.env.n_agents)]\n",
        "\n",
        "    \n",
        "    def update_qtable(self, agent,prev_state, reward, action):\n",
        "      self.q_tables[agent][prev_state,action] = self.q_tables[agent][prev_state,action] + self.alpha * (reward + (self.gamma * np.max(self.q_tables[agent][get_agent_pos(self.env.agent_pos[agent], self.env.resource_collected[agent], self.env.m)])) - self.q_tables[agent][prev_state,action])\n",
        "\n",
        "    def epsilon_decay(elf, time, episodes):\n",
        "      A=0.4 #to explore longer duration\n",
        "      B=0.4 #decides the slope of transition between exploration and exploitation\n",
        "      C=0.1 #controls the steepnes of left and right tails\n",
        "      standardized_time=(time-A*episodes)/(B*episodes)\n",
        "      cosh=np.cosh(math.exp(-standardized_time))\n",
        "      epsilon=1.1-(1/cosh+(time*C/episodes))\n",
        "      return epsilon\n",
        "\n",
        "    def play(self, is_test):\n",
        "        for j in range(self.max_episodes):\n",
        "            self.dones_=[False for i in range(self.env.n_agents)]\n",
        "            self.rewards_per_ep = [0]*self.env.n_agents\n",
        "            reached_at_timestep = [-1]*self.env.n_agents\n",
        "            self.isAllDone = False\n",
        "            self.env.reset()\n",
        "            if is_test:\n",
        "                self.env.render()\n",
        "            self.actions_taken = [[] for h in range(self.env.n_agents)]\n",
        "            while not self.isAllDone:\n",
        "            \n",
        "                self.actions_to_take = []\n",
        "                for i in range(self.env.n_agents):\n",
        "                    self.actions_to_take.append(self.q_agent.step(self.env, self.epsilon, i, self.q_tables[i], is_test))\n",
        "                    self.actions_taken[i].append(self.actions_to_take[i])\n",
        "\n",
        "                prev_states = [get_agent_pos(self.env.agent_pos[agent], self.env.resource_collected[agent], self.env.m) for agent in range(self.env.n_agents)]\n",
        "                obs_n, rew_n, dones_n, info_n = self.env.step(self.actions_to_take)\n",
        "                if is_test:\n",
        "                    self.env.render()\n",
        "                self.rewards_per_ep = np.add(self.rewards_per_ep,rew_n)\n",
        "\n",
        "                for agent in range(self.env.n_agents):\n",
        "                  if not dones_n[i]:\n",
        "                    self.update_qtable(agent, prev_states[agent], rew_n[agent], self.actions_to_take[agent])\n",
        "                \n",
        "                for i,val in enumerate(dones_n):\n",
        "                  if val and (reached_at_timestep[i]==-1):\n",
        "                    reached_at_timestep[i] = self.env.timestep\n",
        "\n",
        "\n",
        "                self.isAllDone = all(dones_n)\n",
        "                if self.isAllDone:\n",
        "                  for i in range(self.env.n_agents):\n",
        "                    if self.reached_at_timestep[i]==-1:\n",
        "                      self.reached_at_timestep[i] = self.env.timestep\n",
        "                self.env.is_agent_silent = [False for i in range(self.env.n_agents)]\n",
        "                if self.env.verbose:\n",
        "                  print(f\"isAllDone={self.isAllDone} at timestep: {self.env.timestep}\")\n",
        "\n",
        "\n",
        "            #epsilon decay\n",
        "            if not is_test:\n",
        "              self.epsilons.append(self.epsilon)\n",
        "              self.epsilon = self.epsilon_decay(j, self.max_episodes)\n",
        "            print(f\"Episode-{j} ended at timestep:{reached_at_timestep}\")\n",
        "            self.env.timestep = 0\n",
        "            for agent in range(self.env.n_agents):\n",
        "              self.rewards_per_eps[agent].append(self.rewards_per_ep[agent])\n",
        "              self.reached_at_timestep[agent].append(reached_at_timestep[agent])\n",
        "        print(self.q_tables)\n",
        "\n",
        "    \n",
        "    def plots(self, x, y, label_, x_label, y_label, clr, do_plot=True):\n",
        "        fig = plt.figure(figsize=(10, 4))\n",
        "        ax = fig.add_subplot(111)\n",
        "        ax.plot(x, y, label=label_, color=clr)\n",
        "        plt.xlabel(x_label)\n",
        "        plt.ylabel(y_label)\n",
        "        plt.legend()\n",
        "            \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_YrpYX2jWFn",
        "outputId": "a2c6d57e-a65b-45f9-c89e-ff81ff2a9793"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Episode-0 ended at timestep:[20, 20, 10]\n",
            "Episode-1 ended at timestep:[20, 20, 20]\n",
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            "Episode-3 ended at timestep:[20, 20, 11]\n",
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            "Episode-238 ended at timestep:[12, 11, 4]\n",
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            "Episode-240 ended at timestep:[8, 9, 5]\n",
            "Episode-241 ended at timestep:[9, 8, 4]\n",
            "Episode-242 ended at timestep:[12, 8, 6]\n",
            "Episode-243 ended at timestep:[8, 17, 7]\n",
            "Episode-244 ended at timestep:[10, 12, 7]\n",
            "Episode-245 ended at timestep:[8, 14, 4]\n",
            "Episode-246 ended at timestep:[8, 20, 5]\n",
            "Episode-247 ended at timestep:[11, 8, 4]\n",
            "Episode-248 ended at timestep:[8, 9, 7]\n",
            "Episode-249 ended at timestep:[10, 13, 5]\n",
            "Episode-250 ended at timestep:[10, 10, 6]\n",
            "Episode-251 ended at timestep:[8, 9, 5]\n",
            "Episode-252 ended at timestep:[8, 11, 9]\n",
            "Episode-253 ended at timestep:[11, 8, 6]\n",
            "Episode-254 ended at timestep:[9, 11, 4]\n",
            "Episode-255 ended at timestep:[14, 8, 6]\n",
            "Episode-256 ended at timestep:[10, 11, 5]\n",
            "Episode-257 ended at timestep:[15, 14, 6]\n",
            "Episode-258 ended at timestep:[8, 10, 5]\n",
            "Episode-259 ended at timestep:[8, 9, 5]\n",
            "Episode-260 ended at timestep:[10, 9, 5]\n",
            "Episode-261 ended at timestep:[8, 9, 5]\n",
            "Episode-262 ended at timestep:[10, 11, 4]\n",
            "Episode-263 ended at timestep:[10, 12, 5]\n",
            "Episode-264 ended at timestep:[8, 12, 5]\n",
            "Episode-265 ended at timestep:[8, 13, 5]\n",
            "Episode-266 ended at timestep:[10, 11, 5]\n",
            "Episode-267 ended at timestep:[12, 12, 4]\n",
            "Episode-268 ended at timestep:[8, 9, 5]\n",
            "Episode-269 ended at timestep:[8, 10, 4]\n",
            "Episode-270 ended at timestep:[12, 11, 4]\n",
            "Episode-271 ended at timestep:[8, 9, 5]\n",
            "Episode-272 ended at timestep:[9, 9, 5]\n",
            "Episode-273 ended at timestep:[8, 11, 5]\n",
            "Episode-274 ended at timestep:[12, 9, 5]\n",
            "Episode-275 ended at timestep:[8, 9, 7]\n",
            "Episode-276 ended at timestep:[10, 11, 5]\n",
            "Episode-277 ended at timestep:[8, 9, 5]\n",
            "Episode-278 ended at timestep:[10, 8, 6]\n",
            "Episode-279 ended at timestep:[10, 11, 4]\n",
            "Episode-280 ended at timestep:[8, 9, 5]\n",
            "Episode-281 ended at timestep:[9, 9, 5]\n",
            "Episode-282 ended at timestep:[10, 11, 5]\n",
            "Episode-283 ended at timestep:[8, 9, 13]\n",
            "Episode-284 ended at timestep:[10, 9, 7]\n",
            "Episode-285 ended at timestep:[8, 10, 7]\n",
            "Episode-286 ended at timestep:[10, 8, 4]\n",
            "Episode-287 ended at timestep:[8, 9, 4]\n",
            "Episode-288 ended at timestep:[10, 10, 4]\n",
            "Episode-289 ended at timestep:[10, 8, 6]\n",
            "Episode-290 ended at timestep:[10, 8, 4]\n",
            "Episode-291 ended at timestep:[10, 9, 7]\n",
            "Episode-292 ended at timestep:[10, 8, 4]\n",
            "Episode-293 ended at timestep:[12, 11, 5]\n",
            "Episode-294 ended at timestep:[8, 9, 4]\n",
            "Episode-295 ended at timestep:[8, 11, 5]\n",
            "Episode-296 ended at timestep:[8, 10, 5]\n",
            "Episode-297 ended at timestep:[8, 9, 5]\n",
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            "Episode-299 ended at timestep:[10, 9, 5]\n",
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            "Episode-304 ended at timestep:[8, 9, 5]\n",
            "Episode-305 ended at timestep:[8, 9, 5]\n",
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            "Episode-307 ended at timestep:[8, 9, 7]\n",
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            "Episode-309 ended at timestep:[10, 8, 4]\n",
            "Episode-310 ended at timestep:[8, 11, 5]\n",
            "Episode-311 ended at timestep:[8, 11, 5]\n",
            "Episode-312 ended at timestep:[8, 9, 5]\n",
            "Episode-313 ended at timestep:[12, 8, 6]\n",
            "Episode-314 ended at timestep:[8, 12, 4]\n",
            "Episode-315 ended at timestep:[9, 12, 4]\n",
            "Episode-316 ended at timestep:[8, 11, 5]\n",
            "Episode-317 ended at timestep:[9, 8, 4]\n",
            "Episode-318 ended at timestep:[8, 12, 4]\n",
            "Episode-319 ended at timestep:[8, 11, 13]\n",
            "Episode-320 ended at timestep:[9, 8, 4]\n",
            "Episode-321 ended at timestep:[10, 11, 4]\n",
            "Episode-322 ended at timestep:[8, 10, 5]\n",
            "Episode-323 ended at timestep:[8, 12, 7]\n",
            "Episode-324 ended at timestep:[8, 9, 7]\n",
            "Episode-325 ended at timestep:[8, 10, 4]\n",
            "Episode-326 ended at timestep:[8, 13, 9]\n",
            "Episode-327 ended at timestep:[8, 10, 4]\n",
            "Episode-328 ended at timestep:[8, 10, 4]\n",
            "Episode-329 ended at timestep:[10, 11, 6]\n",
            "Episode-330 ended at timestep:[8, 9, 7]\n",
            "Episode-331 ended at timestep:[8, 9, 5]\n",
            "Episode-332 ended at timestep:[8, 9, 11]\n",
            "Episode-333 ended at timestep:[10, 9, 5]\n",
            "Episode-334 ended at timestep:[8, 9, 5]\n",
            "Episode-335 ended at timestep:[8, 9, 5]\n",
            "Episode-336 ended at timestep:[8, 9, 7]\n",
            "Episode-337 ended at timestep:[8, 9, 5]\n",
            "Episode-338 ended at timestep:[8, 9, 7]\n",
            "Episode-339 ended at timestep:[8, 10, 5]\n",
            "Episode-340 ended at timestep:[8, 9, 5]\n",
            "Episode-341 ended at timestep:[8, 9, 5]\n",
            "Episode-342 ended at timestep:[8, 9, 7]\n",
            "Episode-343 ended at timestep:[12, 8, 4]\n",
            "Episode-344 ended at timestep:[8, 9, 5]\n",
            "Episode-345 ended at timestep:[8, 9, 5]\n",
            "Episode-346 ended at timestep:[8, 9, 5]\n",
            "Episode-347 ended at timestep:[8, 9, 5]\n",
            "Episode-348 ended at timestep:[8, 14, 4]\n",
            "Episode-349 ended at timestep:[8, 9, 5]\n",
            "Episode-350 ended at timestep:[8, 9, 5]\n",
            "Episode-351 ended at timestep:[8, 9, 5]\n",
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            "Episode-359 ended at timestep:[8, 9, 5]\n",
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            "Episode-373 ended at timestep:[8, 9, 5]\n",
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            "Episode-376 ended at timestep:[8, 9, 5]\n",
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            "Episode-379 ended at timestep:[8, 9, 5]\n",
            "Episode-380 ended at timestep:[8, 9, 5]\n",
            "Episode-381 ended at timestep:[8, 9, 5]\n",
            "Episode-382 ended at timestep:[8, 9, 5]\n",
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            "Episode-384 ended at timestep:[8, 9, 5]\n",
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            "Episode-389 ended at timestep:[8, 9, 5]\n",
            "Episode-390 ended at timestep:[8, 9, 5]\n",
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            "       [-6.73796743e-02,  9.59674151e-01, -1.96707029e-02,\n",
            "        -4.51495112e-02],\n",
            "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
            "         0.00000000e+00],\n",
            "       [ 5.25073759e+00, -2.10937517e-01, -1.88759467e-01,\n",
            "        -3.93087240e-02],\n",
            "       [ 7.39468931e-01, -1.33058208e-01, -1.69045442e-01,\n",
            "        -1.18842064e-01],\n",
            "       [-6.52671380e-02,  9.74850782e-02,  3.97266902e-02,\n",
            "        -6.54064367e-02],\n",
            "       [-1.59631787e-01,  1.81882841e+00,  1.48034430e-01,\n",
            "        -1.44160087e-01],\n",
            "       [-1.65427317e-01, -1.41115598e-01,  1.12680523e+01,\n",
            "         5.14033317e-01],\n",
            "       [ 9.21578325e-01, -1.38815526e-01,  1.94928132e+00,\n",
            "        -2.97307213e-01],\n",
            "       [ 6.19978525e-02, -7.24239837e-02,  2.49170112e-01,\n",
            "        -1.16070468e-01],\n",
            "       [-9.23240840e-02,  1.05811003e-01,  6.21048682e-02,\n",
            "        -8.20475180e-02],\n",
            "       [-2.15924858e-01,  1.09358384e+00,  1.72773427e-01,\n",
            "        -1.52212573e-01],\n",
            "       [-3.48967579e-01, -5.09786147e-01,  1.03496266e+01,\n",
            "         6.77702797e-01],\n",
            "       [ 4.04879630e-01, -3.21794122e-01,  7.81922201e-01,\n",
            "        -1.55581634e-01],\n",
            "       [ 1.07182114e-01, -8.00158639e-02,  1.50343989e-01,\n",
            "        -5.31786445e-02],\n",
            "       [-1.99000000e-02,  2.97010000e-02, -1.00000000e-02,\n",
            "        -2.92042634e-02],\n",
            "       [-1.18014960e-01,  1.00000000e-02, -4.88129401e-02,\n",
            "        -1.71217325e-02],\n",
            "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
            "         0.00000000e+00],\n",
            "       [ 1.00000000e-02, -2.28582895e-01, -2.97010000e-02,\n",
            "        -6.84461104e-02],\n",
            "       [ 8.57409447e-02, -7.54511428e-02, -8.33950936e-02,\n",
            "        -1.97010396e-02],\n",
            "       [-3.89149101e-02,  3.04744162e-02, -2.91149095e-02,\n",
            "        -5.80945378e-02],\n",
            "       [-3.84174728e-02,  6.92941973e-02,  2.54978697e-01,\n",
            "        -6.42474804e-02],\n",
            "       [-3.04485816e-01, -3.12457942e-01,  9.45549800e-01,\n",
            "        -3.50021593e-01],\n",
            "       [ 2.99133755e-02, -1.03683684e-01,  1.53453027e-01,\n",
            "        -7.00569483e-02],\n",
            "       [ 4.36027459e-02, -1.95973039e-02, -5.63427656e-02,\n",
            "        -9.49867535e-03],\n",
            "       [-4.74259438e-02,  5.99532319e-02,  1.98950202e-02,\n",
            "        -1.94971418e-02],\n",
            "       [-9.04625017e-02,  3.80679592e-01,  1.10751838e-01,\n",
            "        -1.57173632e-01],\n",
            "       [-3.64369267e-01, -4.94871151e-01,  1.27419859e+00,\n",
            "        -3.95335310e-01],\n",
            "       [ 3.17723537e-01, -1.38602755e-01,  8.35820182e-02,\n",
            "        -1.19368408e-01],\n",
            "       [ 5.15494327e-02, -9.60175943e-03,  1.99000000e-02,\n",
            "        -6.77140234e-02],\n",
            "       [-1.99000000e-02,  3.04958268e-02,  0.00000000e+00,\n",
            "         0.00000000e+00],\n",
            "       [-6.87992042e-02,  2.25695873e-02,  1.38434354e-01,\n",
            "        -5.68454533e-02],\n",
            "       [-1.00887255e-01, -1.29679133e-01,  3.91281455e-01,\n",
            "        -1.36278374e-01],\n",
            "       [ 8.46173210e-02, -9.30391197e-02,  4.35538599e-02,\n",
            "        -3.80443273e-02],\n",
            "       [ 7.06597357e-02, -9.49518183e-03,  1.08855618e-03,\n",
            "        -3.88130577e-02],\n",
            "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
            "         0.00000000e+00],\n",
            "       [ 0.00000000e+00,  7.14134619e-02,  1.05978807e-02,\n",
            "        -9.69900193e-03],\n",
            "       [-1.16438689e-01, -5.72484800e-02,  1.17133552e-01,\n",
            "        -6.51468268e-02],\n",
            "       [ 6.02029499e-02, -1.96059601e-02,  3.02974068e-02,\n",
            "        -1.97029900e-02],\n",
            "       [ 2.01940399e-02,  0.00000000e+00,  2.97010000e-02,\n",
            "        -1.95021572e-02]])]\n",
            "[[ 30  -1  50  90 100]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ -1  -1 160  -1  -1]\n",
            " [ -1  -1  -1  -1  -1]\n",
            " [ -1  -1  -1  -1  -1]]\n",
            "[[2, 2, 1, 1, 2, 1, 1, 2, 0], [0, 2, 0, 2, 2, 2, 0, 0, 2], [2, 2, 2, 2, 2, 0, 1, 0, 1]]\n",
            "1\n"
          ]
        }
      ],
      "source": [
        "m=n=5\n",
        "n_agents = 3\n",
        "agents = [[4,0], [4,2], [4,4]]\n",
        "goals = [[0,4], [0,2], [0,0]]\n",
        "entities = [10,6, 8]\n",
        "resource_capacity = 180\n",
        "priority = [1,2,2]\n",
        "prioritylevels = [\"H\",\"E\",\"E\"]\n",
        "verbose = False\n",
        "env = GridEnvt( m, n, n_agents, agents, goals, entities, resource_capacity, priority, prioritylevels, 20, verbose)\n",
        "q_l = play_q_learning(env, 0.01, 0.99, 1.0, 400)\n",
        "q_l.play(False)\n",
        "print(env.grid)\n",
        "print(q_l.actions_taken)\n",
        "print(env.agent_res_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "t1iYnospH0EU"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "yKBVzJse7dJg",
        "outputId": "ce51e837-9020-4241-903b-ecc96e40b9b7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(17,5))\n",
        "plt.plot(range(q_l.max_episodes), q_l.rewards_per_eps[0], color='r', label='Agent 1')\n",
        "plt.plot(range(q_l.max_episodes), q_l.rewards_per_eps[1], color='c', label='Agent 2')\n",
        "plt.plot(range(q_l.max_episodes), q_l.rewards_per_eps[2], color='yellow', label='Agent 3')\n",
        "  \n",
        "plt.xlabel(\"Episodes\")\n",
        "plt.ylabel(\"Rewards\")\n",
        "plt.title(\"Q-Learning\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "k1qD8zqv1oHb",
        "outputId": "6ad1b46b-9efc-433e-a2cb-5ceeb588605f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.core.pylabtools import figsize\n",
        "\n",
        "def cumulative_rewards(q_l):\n",
        "      y = [x for x in range(q_l.max_episodes)]\n",
        "      plt.figure(figsize=(15,5))\n",
        "      np.shape(y)\n",
        "      s = 0\n",
        "      cumu_rew = np.zeros([len(y)])\n",
        "      cumu_rew_1 = np.zeros([len(y)])\n",
        "      cumu_rew_2 = np.zeros([len(y)])\n",
        "      for i in range(len(q_l.rewards_per_eps[0])):\n",
        "          s+=q_l.rewards_per_eps[0][i]\n",
        "          cumu_rew[i] = s\n",
        "      s = 0\n",
        "      for i in range(len(q_l.rewards_per_eps[1])):\n",
        "          s+=q_l.rewards_per_eps[1][i]\n",
        "          cumu_rew_1[i] = s\n",
        "      s=0\n",
        "      for i in range(len(q_l.rewards_per_eps[1])):\n",
        "          s+=q_l.rewards_per_eps[2][i]\n",
        "          cumu_rew_2[i] = s\n",
        "      plt.plot(y,cumu_rew, label=\"Agent 1\")\n",
        "      plt.plot(y, cumu_rew_1, label=\"Agent 2\")\n",
        "      plt.plot(y, cumu_rew_2, label=\"Agent 3\")\n",
        "      plt.xlabel('Episodes')\n",
        "      plt.ylabel('Cumulative Reward')\n",
        "      plt.title('Cumulative Reward per Episode')\n",
        "      plt.show()\n",
        "cumulative_rewards(q_l)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WRwNtdus6sbK",
        "outputId": "871aa488-f4e8-467d-9947-65c98dcd3f2b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Episode-0 ended at timestep:[8, 9, 5]\n",
            "Episode-1 ended at timestep:[8, 9, 5]\n",
            "Episode-2 ended at timestep:[8, 9, 5]\n",
            "Episode-3 ended at timestep:[8, 9, 5]\n",
            "Episode-4 ended at timestep:[8, 9, 5]\n",
            "Episode-5 ended at timestep:[8, 9, 5]\n",
            "Episode-6 ended at timestep:[8, 9, 5]\n",
            "Episode-7 ended at timestep:[8, 9, 5]\n",
            "Episode-8 ended at timestep:[8, 9, 5]\n",
            "Episode-9 ended at timestep:[8, 9, 5]\n",
            "[array([[-3.82424835e-02,  5.86345315e-02, -1.99000000e-02,\n",
            "         3.07476779e-02],\n",
            "       [-1.17527870e-01, -7.57390834e-02, -4.93039900e-02,\n",
            "         2.24482342e-02],\n",
            "       [-5.94010000e-02, -1.00000000e-02, -2.94609989e-02,\n",
            "         8.76047734e-02],\n",
            "       [-1.96599989e-02, -3.00000000e-02, -1.00000000e-02,\n",
            "         0.00000000e+00],\n",
            "       [ 1.00000000e-02,  0.00000000e+00,  0.00000000e+00,\n",
            "         0.00000000e+00],\n",
            "       [-8.53867156e-02,  1.69352632e-01, -2.01982411e-01,\n",
            "         5.38381953e-01],\n",
            "       [-1.19646580e-01,  1.47369447e-01, -5.84773368e-02,\n",
            "         6.58768220e-01],\n",
            "       [-5.30509023e-02, -8.52534899e-02, -8.84766405e-02,\n",
            "         1.04305312e+00],\n",
            "       [ 5.07771355e-02, -1.00000000e-02, -1.00000000e-02,\n",
            "        -8.90342821e-03],\n",
            "       [ 1.03146829e-02,  0.00000000e+00,  0.00000000e+00,\n",
            "         0.00000000e+00],\n",
            "       [ 1.86872595e-01,  9.07385516e+00, -3.14097357e-01,\n",
            "        -9.80077937e-02],\n",
            "       [ 7.00588710e-02,  1.03377284e+01, -2.27175846e-01,\n",
            "        -1.43267133e-01],\n",
            "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
            "         0.00000000e+00],\n",
            "       [ 3.77910343e-01, -1.00000000e-02, -1.95853171e-02,\n",
            "        -2.90188307e-02],\n",
            "       [-7.13529240e-03,  0.00000000e+00,  0.00000000e+00,\n",
            "         0.00000000e+00],\n",
            "       [-1.33957669e-01,  6.11738134e-01,  7.40618239e+00,\n",
            "        -5.96097625e-03],\n",
            "       [-1.17933577e-01,  2.50198916e-01,  2.56556368e+00,\n",
            "        -2.19637748e-01],\n",
            "       [-4.96658323e-02, -1.23389457e-01,  1.67766053e+00,\n",
            "        -1.25723697e-01],\n",
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            "       [ 2.01940399e-02,  0.00000000e+00,  2.97010000e-02,\n",
            "        -1.95021572e-02]])]\n"
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        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Testing\n",
        "q_l.reset(True)\n",
        "q_l.max_episodes = 10\n",
        "q_l.play(True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "a0G4C4V6625V"
      },
      "outputs": [],
      "source": [
        "frame_folder = \"frames\"\n",
        "def make_gif(frame_folder, save_gif):\n",
        "    frames = list()\n",
        "    images_list = glob.glob(f\"{frame_folder}/*.png\")\n",
        "    images_list.sort()\n",
        "    pause = 5\n",
        "    count = 0\n",
        "    for image in images_list:\n",
        "        frames.append(Image.open(image))\n",
        "\n",
        "    frame_one = frames[0]\n",
        "    frame_one.save(save_gif+\".gif\", format=\"GIF\", append_images=frames,\n",
        "                   save_all=True, duration=800, loop=0)\n",
        "make_gif(frame_folder, \"ql\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "f8M6O_MyGFmY",
        "outputId": "973be5bc-aef4-46c5-a01a-c3b39e7baeb0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(17,5))\n",
        "plt.plot(range(q_l.max_episodes), q_l.rewards_per_eps[0], color='r', label='Agent 1')\n",
        "plt.plot(range(q_l.max_episodes), q_l.rewards_per_eps[1], color='c', label='Agent 2')\n",
        "plt.plot(range(q_l.max_episodes), q_l.rewards_per_eps[2], color='yellow', label='Agent 3')\n",
        "  \n",
        "# Naming the x-axis, y-axis and the whole graph\n",
        "plt.xlabel(\"Episodes\")\n",
        "plt.ylabel(\"Rewards\")\n",
        "plt.title(\"Q-Learning\")\n",
        "  \n",
        "# Adding legend, which helps us recognize the curve according to it's color\n",
        "plt.legend()\n",
        "  \n",
        "# To load the display window\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "gRSoNqVN_3XV"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9ar0KopOEMz"
      },
      "source": [
        "*References*\n",
        "\n",
        "1. CSE 546 Lecture Slides\n",
        "\n",
        "2. [Learning Fairness in Multi-Agent Systems](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fproceedings.neurips.cc%2Fpaper%2F2019%2Ffile%2F10493aa88605cad5ab4752b04a63d172-Paper.pdf&data=04%7C01%7Cnandinic%40buffalo.edu%7C7b1343e15c4b4ca9c35808da1a39faf3%7C96464a8af8ed40b199e25f6b50a20250%7C0%7C0%7C637851134019482795%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=Nwq0AjAZVjXJgSt2sHeaSR0anLEjddPcGadXePLkZuE%3D&reserved=0)\n",
        "\n",
        "3. [Stability, Fairness, and Scalability of Multi-Agent Systems](https://nam12.safelinks.protection.outlook.com/?url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.6.1713%26rep%3Drep1%26type%3Dpdf&data=04%7C01%7Cnandinic%40buffalo.edu%7C7b1343e15c4b4ca9c35808da1a39faf3%7C96464a8af8ed40b199e25f6b50a20250%7C0%7C0%7C637851134019482795%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=sftrUSN%2FLrqXu3DRbO3%2BeIBWN8rycb9SY8Vp%2FQ4pYT0%3D&reserved=0)\n",
        "\n",
        "4. [Reinforcement Learning as a Framework for Ethical Decision Making](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.aaai.org%2Focs%2Findex.php%2FWS%2FAAAIW16%2Fpaper%2Fview%2F12582%2F12346&data=04%7C01%7Cnandinic%40buffalo.edu%7C7b1343e15c4b4ca9c35808da1a39faf3%7C96464a8af8ed40b199e25f6b50a20250%7C0%7C0%7C637851134019482795%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=67Cx1zVkJgkkmSle3MhrsBhRhS18hzfZ%2BMBPlZzuGWI%3D&reserved=0)\n",
        "\n",
        "5. [Fairness in Multi-Agent Sequential Decision Making](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fproceedings.neurips.cc%2Fpaper%2F2014%2Ffile%2F792c7b5aae4a79e78aaeda80516ae2ac-Paper.pdf&data=04%7C01%7Cnandinic%40buffalo.edu%7C7b1343e15c4b4ca9c35808da1a39faf3%7C96464a8af8ed40b199e25f6b50a20250%7C0%7C0%7C637851134019482795%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=HgJuNKagqWjgvtu7TeGgytIF8juIAyJwscZXHqTRxRM%3D&reserved=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7NPrM8FwOo-u"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "DDQN_multi_agent_ddqn_3agents_v1.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.5 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.5"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "23dbfd74957891d165059e99eec926bd190c592231f8e084d44d01c13e273430"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}