DRL4TSP (LSTM, A2C).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ca96a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入库\n",
    "import os\n",
    "import math\n",
    "import argparse\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.utils.tensorboard import SummaryWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0d1c849",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义TSPDataset类\n",
    "class TSPDataset(Dataset):\n",
    "    def __init__(self, num_nodes, num_samples, random_seed=42):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.num_nodes = num_nodes\n",
    "        self.num_samples = num_samples\n",
    "        self.random_seed = random_seed\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.num_samples\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        seed = self.random_seed + idx\n",
    "        generator = torch.Generator()\n",
    "        generator.manual_seed(seed)\n",
    "        sample = torch.rand(self.num_nodes, 2, generator=generator)\n",
    "        return sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c479b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义reward函数\n",
    "def reward(sols):\n",
    "    # sols: (batch_size, seq_len, input_size)\n",
    "    # lengths: (batch_size,)\n",
    "    lengths = torch.norm(sols[:, 1:, :] - sols[:, :-1, :], dim=2).sum(dim=1)\n",
    "    lengths += torch.norm(sols[:, -1, :] - sols[:, 0, :], dim=1)\n",
    "    return lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01f324ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义Embedding类\n",
    "class Embedding(nn.Module):\n",
    "    def __init__(self, input_size, embedding_size):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.embedding = nn.Linear(input_size, embedding_size)\n",
    "        \n",
    "    def forward(self, inputs):\n",
    "        # inputs: (batch_size, seq_len, input_size)\n",
    "        # embedded: (batch_size, seq_len, embedding_size)\n",
    "        embedded = self.embedding(inputs)\n",
    "        return embedded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30ad2876",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义Attention类\n",
    "class Attention(nn.Module):\n",
    "    def __init__(self, hidden_size):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.W_ref = nn.Conv1d(hidden_size, hidden_size, 1, 1)\n",
    "        self.W_q = nn.Linear(hidden_size, hidden_size)\n",
    "        self.V = nn.Parameter(torch.FloatTensor(hidden_size))\n",
    "        std = 1. / math.sqrt(hidden_size)\n",
    "        torch.nn.init.uniform_(self.V, -std, std)\n",
    "        \n",
    "    def forward(self, ref, query):\n",
    "        # ref: (batch_size, seq_len, hidden_size)\n",
    "        # query: (batch_size, hidden_size)\n",
    "        batch_size, seq_len, _ = ref.size()\n",
    "        # ref: (batch_size, hidden_size, seq_len)\n",
    "        ref = ref.permute(0, 2, 1)\n",
    "        # ref: (batch_size, hidden_size, seq_len)\n",
    "        ref = self.W_ref(ref)\n",
    "        # query: (batch_size, hidden_size, 1)\n",
    "        query = self.W_q(query).unsqueeze(2)\n",
    "        # expanded_query: (batch_size, hidden_size, seq_len)\n",
    "        expanded_query = query.repeat(1, 1, seq_len)\n",
    "        # V: (batch_size, 1, hidden_size)\n",
    "        V = self.V.unsqueeze(0).unsqueeze(0).expand(batch_size, -1, -1)\n",
    "        # logits: (batch_size, seq_len)\n",
    "        logits = torch.bmm(V, torch.tanh(ref + expanded_query)).squeeze(1)\n",
    "        \n",
    "        return ref, logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "934547c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义PointerNetwork类\n",
    "class PtrNet(nn.Module):\n",
    "    def __init__(self, embedding_size, hidden_size, seq_len, n_glimpses, use_temperature, T0):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.embedding_size = embedding_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.seq_len = seq_len\n",
    "        self.n_glimpses = n_glimpses\n",
    "        self.use_temperature = use_temperature\n",
    "        self.temperature = T0\n",
    "        \n",
    "        self.embedding = Embedding(2, embedding_size)\n",
    "        self.encoder = nn.LSTM(embedding_size, hidden_size, batch_first=True)\n",
    "        self.decoder = nn.LSTM(embedding_size, hidden_size, batch_first=True)\n",
    "        self.pointer = Attention(hidden_size)\n",
    "        self.glimpse = Attention(hidden_size)\n",
    "        \n",
    "        self.decoder_init_input = nn.Parameter(torch.FloatTensor(embedding_size))\n",
    "        std = 1. / math.sqrt(embedding_size)\n",
    "        nn.init.uniform_(self.decoder_init_input, -std, std)\n",
    "        \n",
    "    def apply_mask_to_logits(self, logits, mask, idxs):\n",
    "        # logits, mask: (batch_size, seq_len)\n",
    "        # idxs: (batch_size,)\n",
    "        batch_size = logits.size(0)\n",
    "        clone_mask = mask.clone().detach()\n",
    "        \n",
    "        if idxs is not None:\n",
    "            clone_mask[torch.arange(batch_size), idxs] = 1\n",
    "            logits = logits.masked_fill(clone_mask.bool(), float('-inf'))\n",
    "        return logits, clone_mask\n",
    "    \n",
    "    def forward(self, inputs):\n",
    "        # inputs: (batch_size, seq_len, input_size)\n",
    "        batch_size, seq_len, _ = inputs.size()\n",
    "        assert seq_len == self.seq_len\n",
    "        \n",
    "        prev_probs, prev_idxs = [], []\n",
    "        idxs = None\n",
    "        # masks: (batch_size, seq_len)\n",
    "        mask = torch.zeros(batch_size, seq_len, dtype=torch.bool, device=inputs.device)\n",
    "        \n",
    "        # embedded: (batch_size, seq_len, embedding_size)\n",
    "        embedded = self.embedding(inputs)\n",
    "        \n",
    "        # encoder_output: (batch_size, seq_len, hidden_size)\n",
    "        # hidden, context: (num_layers, batch_size, hidden_size)\n",
    "        encoder_output, (hidden, context) = self.encoder(embedded)\n",
    "        \n",
    "        # decoder_input: (batch_size, embedded_size)\n",
    "        decoder_input = self.decoder_init_input.unsqueeze(0).expand(batch_size, -1)\n",
    "        \n",
    "        for _ in range(seq_len):\n",
    "            # hidden, context: (num_layers, batch_size, embedding_size)\n",
    "            _, (hidden, context) = self.decoder(decoder_input.unsqueeze(1), (hidden, context))\n",
    "            # query: (batch_size, embedding_size)\n",
    "            query = hidden.squeeze(0)\n",
    "            \n",
    "            for _ in range(self.n_glimpses):\n",
    "                # ref: (batch_size, hidden_size, seq_len)\n",
    "                # logits: (batch_size, seq_len)\n",
    "                ref, logits = self.glimpse(encoder_output, query)\n",
    "                # logits, mask: (batch_size, seq_len)\n",
    "                logits, mask = self.apply_mask_to_logits(logits, mask, idxs)\n",
    "                # query: (batch_size, hidden_size)\n",
    "                query = torch.bmm(ref, F.softmax(logits, dim=1).unsqueeze(2)).squeeze(2)\n",
    "            \n",
    "            # logits = (batch_size, seq_len)\n",
    "            _, logits = self.pointer(encoder_output, query)\n",
    "            # logits, mask: (batch_size, seq_len)\n",
    "            logits, mask = self.apply_mask_to_logits(logits, mask, idxs)\n",
    "            # probs: (batch_size, seq_len)\n",
    "            if self.use_temperature and self.training:\n",
    "                probs = F.softmax(logits / self.temperature, dim=1)\n",
    "            else:\n",
    "                probs = F.softmax(logits, dim=1)\n",
    "            # idxs: (batch_size,)\n",
    "            idxs = torch.multinomial(probs, 1).squeeze(1)\n",
    "            \n",
    "            for old_idxs in prev_idxs:\n",
    "                if old_idxs.eq(idxs).any():\n",
    "                    idxs = torch.multinomial(probs, 1).squeeze(1)\n",
    "                    \n",
    "            # decoder_input: (batch_size, embedding_size)\n",
    "            decoder_input = embedded[torch.arange(batch_size), idxs, :]\n",
    "            \n",
    "            prev_probs.append(probs[torch.arange(batch_size), idxs])\n",
    "            prev_idxs.append(idxs)\n",
    "        \n",
    "        # prev_probs, prev_idxs: [seq_len, (batch_size,)]\n",
    "        return prev_probs, prev_idxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a50d02b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义Actor类\n",
    "class Actor(nn.Module):\n",
    "    def __init__(self, embedding_size, hidden_size, seq_len, n_glimpses, use_temperature, T0, reward):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.reward = reward\n",
    "        self.ptr = PtrNet(embedding_size, hidden_size, seq_len, n_glimpses, use_temperature, T0)\n",
    "        \n",
    "    def forward(self, inputs):\n",
    "        # inputs: (batch_size, seq_len, input_size)\n",
    "        batch_size, seq_len, input_size = inputs.size()\n",
    "        \n",
    "        # probs, idxs: [seq_len, (batch_size,)]\n",
    "        probs, idxs = self.ptr(inputs)\n",
    "        # probs: (batch_size, seq_len)\n",
    "        probs = torch.stack(probs, dim=1)\n",
    "        # idxs: (batch_size, seq_len)\n",
    "        idxs = torch.stack(idxs, dim=1)\n",
    "        # actions: (batch_size, seq_len, input_size)\n",
    "        actions = inputs[torch.arange(batch_size).unsqueeze(1), idxs]\n",
    "        # reward_values: (batch_size,)\n",
    "        reward_values = self.reward(actions)\n",
    "        return reward_values, idxs, probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ed5f0089",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义Critic类\n",
    "class Critic(nn.Module):\n",
    "    def __init__(self, embedding_size, hidden_size, n_process_blocks):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.n_process_blocks = n_process_blocks\n",
    "        \n",
    "        self.embedding = Embedding(2, embedding_size)\n",
    "        self.encoder = nn.LSTM(embedding_size, hidden_size, batch_first=True)\n",
    "        self.process_block = Attention(hidden_size)\n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.Linear(hidden_size, hidden_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_size, 1)\n",
    "        )\n",
    "    \n",
    "    def forward(self, inputs):\n",
    "        # inputs: (batch_size, seq_len, input_size)\n",
    "        # embedded: (batch_size, seq_len, embedding_size)\n",
    "        embedded = self.embedding(inputs)\n",
    "        \n",
    "        # encoder_output: (batch_size, seq_len, hidden_size)\n",
    "        # hidden, context: (num_layers, batch_size, hidden_size)\n",
    "        encoder_output, (hidden, context) = self.encoder(embedded)\n",
    "        # query: (batch_size, hidden_size)\n",
    "        query = hidden.squeeze(0)\n",
    "        \n",
    "        for _ in range(self.n_process_blocks):\n",
    "            # ref: (batch_size, hidden_size, seq_len)\n",
    "            # logits: (batch_size, seq_len)\n",
    "            ref, logits = self.process_block(encoder_output, query)\n",
    "            # query: (batch_size, hidden_size)\n",
    "            query = torch.bmm(ref, F.softmax(logits, dim=1).unsqueeze(2)).squeeze(2)\n",
    "        \n",
    "        # critic_value: (batch_size,)\n",
    "        critic_value = self.decoder(query).squeeze(1)\n",
    "        return critic_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "596cc836",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练\n",
    "def train(args, train_dataset, val_dataset):\n",
    "    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False)\n",
    "    \n",
    "    actor = Actor(args.embedding_size, args.hidden_size, args.seq_len, args.n_glimpses, args.use_temperature, args.T0, reward).to(args.device)\n",
    "    critic = Critic(args.embedding_size, args.hidden_size, args.n_process_blocks).to(args.device)\n",
    "    actor_optim = optim.Adam(actor.parameters(), lr=args.lr)\n",
    "    critic_optim = optim.Adam(critic.parameters(), lr=args.lr)\n",
    "    \n",
    "    step = 0\n",
    "    best_length = float('inf')\n",
    "    train_lengths, val_lengths = [], []\n",
    "    writer = SummaryWriter()\n",
    "    model_path = os.path.join(args.output_dir, args.name)\n",
    "    \n",
    "    for epoch in range(args.n_epochs):\n",
    "        for batch_id, train_batch in enumerate(train_loader):\n",
    "            actor.train()\n",
    "            critic.train()\n",
    "            # inputs: (batch_size, seq_len, input_size)\n",
    "            inputs = train_batch.to(args.device)\n",
    "            # reward_values: (batch_size,)\n",
    "            # idxs, probs: (batch_size, seq_len)\n",
    "            reward_values, idxs, probs = actor(inputs)\n",
    "            # log_probs: (batch_size,)\n",
    "            log_probs = torch.log(probs.clamp(min=1e-10)).sum(dim=1)\n",
    "            log_probs = torch.clamp(log_probs, min=-100)\n",
    "            # critic_values: (batch_size,)\n",
    "            critic_values = critic(inputs)\n",
    "            # advantage: (batch_size,)\n",
    "            advantage = reward_values - critic_values.detach()\n",
    "            # reinforce: (batch_size,)\n",
    "            reinforce = advantage * log_probs\n",
    "            # actor_loss: (1)\n",
    "            actor_loss = reinforce.mean()\n",
    "            # critic_loss: (1)\n",
    "            critic_loss = F.mse_loss(critic_values, reward_values.detach())\n",
    "            \n",
    "            actor_optim.zero_grad()\n",
    "            actor_loss.backward()\n",
    "            torch.nn.utils.clip_grad_norm_(actor.parameters(), args.max_grad_norm)\n",
    "            actor_optim.step()\n",
    "            \n",
    "            critic_optim.zero_grad()\n",
    "            critic_loss.backward()\n",
    "            torch.nn.utils.clip_grad_norm_(critic.parameters(), args.max_grad_norm)\n",
    "            critic_optim.step()\n",
    "            \n",
    "            writer.add_scalar('Actor_Loss/train', actor_loss.item(), step)\n",
    "            writer.add_scalar('Critic_Loss/train', critic_loss.item(), step)\n",
    "            writer.add_scalar('Length/train', reward_values.mean().item(), step)\n",
    "            step += 1\n",
    "            \n",
    "            if batch_id % 100 == 0:\n",
    "                print(f\"Epoch [{epoch + 1}/{args.n_epochs}], Batch [{batch_id + 1}/{len(train_loader)}], \"\n",
    "                      f\"Actor Loss: {actor_loss.item():.4f}, Critic Loss: {critic_loss.item():.4f}, \"\n",
    "                      f\"Reward: {reward_values.mean().item():.4f}\")\n",
    "                actor.eval()\n",
    "                critic.eval()\n",
    "                with torch.no_grad():\n",
    "                    total_rewards, total_nums = 0, 0\n",
    "                    for val_batch in val_loader:\n",
    "                        inputs = val_batch.to(args.device)\n",
    "                        actor_rewards, _, _ = actor(inputs)\n",
    "                        total_rewards += actor_rewards.sum().item()\n",
    "                        total_nums += inputs.size(0)\n",
    "                    avg_reward = total_rewards / total_nums\n",
    "                    writer.add_scalar('Length/validation', avg_reward, step)\n",
    "                    print(f\"Validation average reward after epoch {epoch + 1}: {avg_reward:.4f}\")\n",
    "                    \n",
    "                    if avg_reward < best_length:\n",
    "                        best_length = avg_reward\n",
    "                        torch.save(actor.state_dict(), model_path)\n",
    "                    \n",
    "                    writer.add_scalar('Best length/validation', best_length, step)\n",
    "        \n",
    "        actor.ptr.temperature = args.T0 * (1 - epoch / (args.n_epochs - 1))\n",
    "            \n",
    "    writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6151f39e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 画TSP示例图\n",
    "def plot_tsp_paths(args, inputs, idxs, rewards):\n",
    "    # inputs: (batch_size, seq_len, 2)\n",
    "    # idxs: (batch_size, seq_len)\n",
    "    # rewards: (batch_size,)\n",
    "    inputs = inputs.cpu().numpy()\n",
    "    idxs = idxs.cpu().numpy()\n",
    "    rewards = rewards.cpu().numpy()\n",
    "    \n",
    "    fig, axes = plt.subplots(2, 4, figsize=(16, 8))\n",
    "    fig.suptitle(args.name)\n",
    "    \n",
    "    for i in range(8):\n",
    "        ax = axes[i // 4, i % 4]\n",
    "        coords = inputs[i]\n",
    "        path = idxs[i]\n",
    "        \n",
    "        for j in range(len(path)):\n",
    "            x1, y1 = coords[path[j - 1]]\n",
    "            x2, y2 = coords[path[j]]\n",
    "            ax.plot([x1, x2], [y1, y2], 'bo-')\n",
    "        \n",
    "        ax.scatter(coords[:, 0], coords[:, 1], c='red')\n",
    "        ax.scatter(coords[0, 0], coords[0, 1], c='yellow')\n",
    "        ax.set_title(f'Length: {rewards[i]:.3f}')\n",
    "    \n",
    "    plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7936e31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试\n",
    "def test1(args, test_dataset):\n",
    "    test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False)\n",
    "    \n",
    "    actor = Actor(args.embedding_size, args.hidden_size, args.seq_len, args.n_glimpses, False, args.T0, reward).to(args.device)\n",
    "    actor.load_state_dict(torch.load(os.path.join(args.output_dir, args.name)))\n",
    "    \n",
    "    actor.eval()\n",
    "    total_rewards, total_nums = 0, 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for i, test_batch in enumerate(test_loader):\n",
    "            inputs = test_batch.to(args.device)\n",
    "            actor_rewards, idxs, _ = actor(inputs)\n",
    "            total_rewards += actor_rewards.sum().item()\n",
    "            total_nums += inputs.size(0)\n",
    "            \n",
    "            if i == 0 and inputs.size(0) >= 8:\n",
    "                plot_tsp_paths(args, inputs[:8], idxs[:8], actor_rewards[:8])\n",
    "            \n",
    "    avg_reward = total_rewards / total_nums\n",
    "    print(f\"Test average reward (greedy): {avg_reward:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11678c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义参数\n",
    "args = argparse.Namespace()\n",
    "args.n_epochs = 20\n",
    "args.batch_size = 128\n",
    "args.lr = 1e-3\n",
    "args.embedding_size = 128\n",
    "args.hidden_size = 128\n",
    "args.seq_len = 20\n",
    "args.n_glimpses = 1\n",
    "args.use_temperature = True\n",
    "args.T0 = 5 \n",
    "args.T = 2.\n",
    "args.num_samples = 12800\n",
    "args.n_process_blocks = 3\n",
    "args.max_grad_norm = 1.\n",
    "args.output_dir = 'output'\n",
    "args.name = 'DRL4TSP_A2C_TSP' + str(args.seq_len)\n",
    "args.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "\n",
    "os.makedirs(args.output_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "faf15a8a-a486-41f4-9bf6-97aac040e239",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成数据集\n",
    "train_dataset = TSPDataset(args.seq_len, 1000000)\n",
    "val_dataset = TSPDataset(args.seq_len, 1000)\n",
    "test_dataset = TSPDataset(args.seq_len, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "114c4e43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training started...\n",
      "Epoch [1/20], Batch [1/7813], Actor Loss: -441.3340, Critic Loss: 109.9810, Reward: 10.4567\n",
      "Validation average reward after epoch 1: 10.3849\n",
      "Epoch [1/20], Batch [101/7813], Actor Loss: -15.9170, Critic Loss: 1.5334, Reward: 10.4560\n",
      "Validation average reward after epoch 1: 10.3992\n",
      "Epoch [1/20], Batch [201/7813], Actor Loss: 13.1607, Critic Loss: 1.7266, Reward: 10.4329\n",
      "Validation average reward after epoch 1: 10.3678\n",
      "Epoch [1/20], Batch [301/7813], Actor Loss: -5.8749, Critic Loss: 1.3114, Reward: 8.0206\n",
      "Validation average reward after epoch 1: 7.4932\n",
      "Epoch [1/20], Batch [401/7813], Actor Loss: -1.4693, Critic Loss: 0.8233, Reward: 7.1704\n",
      "Validation average reward after epoch 1: 6.6849\n",
      "Epoch [1/20], Batch [501/7813], Actor Loss: -5.9504, Critic Loss: 0.6515, Reward: 6.0647\n",
      "Validation average reward after epoch 1: 5.1705\n",
      "Epoch [1/20], Batch [601/7813], Actor Loss: 3.2133, Critic Loss: 0.5199, Reward: 5.5644\n",
      "Validation average reward after epoch 1: 4.8665\n",
      "Epoch [1/20], Batch [701/7813], Actor Loss: -4.6312, Critic Loss: 0.3351, Reward: 5.3098\n",
      "Validation average reward after epoch 1: 4.7139\n",
      "Epoch [1/20], Batch [801/7813], Actor Loss: 1.4332, Critic Loss: 0.3652, Reward: 4.9776\n",
      "Validation average reward after epoch 1: 4.4735\n",
      "Epoch [1/20], Batch [901/7813], Actor Loss: -2.5677, Critic Loss: 0.2954, Reward: 4.8770\n",
      "Validation average reward after epoch 1: 4.3389\n",
      "Epoch [1/20], Batch [1001/7813], Actor Loss: 4.5090, Critic Loss: 0.3852, Reward: 4.5847\n",
      "Validation average reward after epoch 1: 4.3025\n",
      "Epoch [1/20], Batch [1101/7813], Actor Loss: -0.4507, Critic Loss: 0.2165, Reward: 4.6208\n",
      "Validation average reward after epoch 1: 4.2481\n",
      "Epoch [1/20], Batch [1201/7813], Actor Loss: 2.2500, Critic Loss: 0.2741, Reward: 4.5857\n",
      "Validation average reward after epoch 1: 4.2927\n",
      "Epoch [1/20], Batch [1301/7813], Actor Loss: 0.3177, Critic Loss: 0.1726, Reward: 4.5257\n",
      "Validation average reward after epoch 1: 4.2453\n",
      "Epoch [1/20], Batch [1401/7813], Actor Loss: 0.1929, Critic Loss: 0.1652, Reward: 4.5012\n",
      "Validation average reward after epoch 1: 4.2356\n",
      "Epoch [1/20], Batch [1501/7813], Actor Loss: -2.2777, Critic Loss: 0.1751, Reward: 4.5529\n",
      "Validation average reward after epoch 1: 4.2444\n",
      "Epoch [1/20], Batch [1601/7813], Actor Loss: -1.8193, Critic Loss: 0.1906, Reward: 4.4425\n",
      "Validation average reward after epoch 1: 4.1879\n",
      "Epoch [1/20], Batch [1701/7813], Actor Loss: -1.5499, Critic Loss: 0.1561, Reward: 4.3362\n",
      "Validation average reward after epoch 1: 4.1882\n",
      "Epoch [1/20], Batch [1801/7813], Actor Loss: -0.9026, Critic Loss: 0.1723, Reward: 4.4032\n",
      "Validation average reward after epoch 1: 4.1841\n",
      "Epoch [1/20], Batch [1901/7813], Actor Loss: 0.1041, Critic Loss: 0.1720, Reward: 4.3354\n",
      "Validation average reward after epoch 1: 4.1755\n",
      "Epoch [1/20], Batch [2001/7813], Actor Loss: -0.8068, Critic Loss: 0.1331, Reward: 4.3490\n",
      "Validation average reward after epoch 1: 4.1730\n",
      "Epoch [1/20], Batch [2101/7813], Actor Loss: -1.1551, Critic Loss: 0.1299, Reward: 4.3229\n",
      "Validation average reward after epoch 1: 4.1611\n",
      "Epoch [1/20], Batch [2201/7813], Actor Loss: -0.9286, Critic Loss: 0.1662, Reward: 4.3432\n",
      "Validation average reward after epoch 1: 4.1630\n",
      "Epoch [1/20], Batch [2301/7813], Actor Loss: 0.1944, Critic Loss: 0.1383, Reward: 4.3334\n",
      "Validation average reward after epoch 1: 4.1685\n",
      "Epoch [1/20], Batch [2401/7813], Actor Loss: 0.0344, Critic Loss: 0.1080, Reward: 4.2322\n",
      "Validation average reward after epoch 1: 4.1442\n",
      "Epoch [1/20], Batch [2501/7813], Actor Loss: -0.7206, Critic Loss: 0.1176, Reward: 4.2980\n",
      "Validation average reward after epoch 1: 4.1442\n",
      "Epoch [1/20], Batch [2601/7813], Actor Loss: 0.0434, Critic Loss: 0.1211, Reward: 4.2541\n",
      "Validation average reward after epoch 1: 4.1401\n",
      "Epoch [1/20], Batch [2701/7813], Actor Loss: 0.1766, Critic Loss: 0.0951, Reward: 4.2708\n",
      "Validation average reward after epoch 1: 4.1582\n",
      "Epoch [1/20], Batch [2801/7813], Actor Loss: -0.0584, Critic Loss: 0.1159, Reward: 4.2368\n",
      "Validation average reward after epoch 1: 4.1338\n",
      "Epoch [1/20], Batch [2901/7813], Actor Loss: -0.0377, Critic Loss: 0.0934, Reward: 4.2868\n",
      "Validation average reward after epoch 1: 4.1265\n",
      "Epoch [1/20], Batch [3001/7813], Actor Loss: -1.1940, Critic Loss: 0.1189, Reward: 4.2763\n",
      "Validation average reward after epoch 1: 4.1438\n",
      "Epoch [1/20], Batch [3101/7813], Actor Loss: -0.3452, Critic Loss: 0.1026, Reward: 4.2110\n",
      "Validation average reward after epoch 1: 4.1379\n",
      "Epoch [1/20], Batch [3201/7813], Actor Loss: -0.7652, Critic Loss: 0.1381, Reward: 4.3161\n",
      "Validation average reward after epoch 1: 4.1220\n",
      "Epoch [1/20], Batch [3301/7813], Actor Loss: -0.7165, Critic Loss: 0.1027, Reward: 4.2380\n",
      "Validation average reward after epoch 1: 4.1369\n",
      "Epoch [1/20], Batch [3401/7813], Actor Loss: -0.8121, Critic Loss: 0.1098, Reward: 4.2701\n",
      "Validation average reward after epoch 1: 4.1194\n",
      "Epoch [1/20], Batch [3501/7813], Actor Loss: -0.6999, Critic Loss: 0.0797, Reward: 4.2655\n",
      "Validation average reward after epoch 1: 4.0996\n",
      "Epoch [1/20], Batch [3601/7813], Actor Loss: -0.0507, Critic Loss: 0.0893, Reward: 4.1997\n",
      "Validation average reward after epoch 1: 4.1035\n",
      "Epoch [1/20], Batch [3701/7813], Actor Loss: -0.6402, Critic Loss: 0.0806, Reward: 4.2264\n",
      "Validation average reward after epoch 1: 4.1123\n",
      "Epoch [1/20], Batch [3801/7813], Actor Loss: 0.4682, Critic Loss: 0.1020, Reward: 4.2217\n",
      "Validation average reward after epoch 1: 4.1241\n",
      "Epoch [1/20], Batch [3901/7813], Actor Loss: 0.4531, Critic Loss: 0.0683, Reward: 4.1650\n",
      "Validation average reward after epoch 1: 4.1084\n",
      "Epoch [1/20], Batch [4001/7813], Actor Loss: -0.1372, Critic Loss: 0.0951, Reward: 4.1569\n",
      "Validation average reward after epoch 1: 4.1037\n",
      "Epoch [1/20], Batch [4101/7813], Actor Loss: -0.5659, Critic Loss: 0.0697, Reward: 4.1867\n",
      "Validation average reward after epoch 1: 4.0945\n",
      "Epoch [1/20], Batch [4201/7813], Actor Loss: -0.7888, Critic Loss: 0.0843, Reward: 4.2074\n",
      "Validation average reward after epoch 1: 4.0968\n",
      "Epoch [1/20], Batch [4301/7813], Actor Loss: 0.1236, Critic Loss: 0.0830, Reward: 4.1642\n",
      "Validation average reward after epoch 1: 4.1000\n",
      "Epoch [1/20], Batch [4401/7813], Actor Loss: -0.4266, Critic Loss: 0.0716, Reward: 4.1680\n",
      "Validation average reward after epoch 1: 4.0919\n",
      "Epoch [1/20], Batch [4501/7813], Actor Loss: -0.3396, Critic Loss: 0.0780, Reward: 4.2003\n",
      "Validation average reward after epoch 1: 4.0841\n",
      "Epoch [1/20], Batch [4601/7813], Actor Loss: -0.4343, Critic Loss: 0.0646, Reward: 4.1804\n",
      "Validation average reward after epoch 1: 4.0984\n",
      "Epoch [1/20], Batch [4701/7813], Actor Loss: -0.1393, Critic Loss: 0.0718, Reward: 4.1780\n",
      "Validation average reward after epoch 1: 4.0843\n",
      "Epoch [1/20], Batch [4801/7813], Actor Loss: -0.2932, Critic Loss: 0.0866, Reward: 4.1191\n",
      "Validation average reward after epoch 1: 4.1002\n",
      "Epoch [1/20], Batch [4901/7813], Actor Loss: -0.7275, Critic Loss: 0.0849, Reward: 4.1992\n",
      "Validation average reward after epoch 1: 4.1116\n",
      "Epoch [1/20], Batch [5001/7813], Actor Loss: -0.0827, Critic Loss: 0.0546, Reward: 4.1476\n",
      "Validation average reward after epoch 1: 4.0784\n",
      "Epoch [1/20], Batch [5101/7813], Actor Loss: -0.4186, Critic Loss: 0.0696, Reward: 4.1258\n",
      "Validation average reward after epoch 1: 4.0677\n",
      "Epoch [1/20], Batch [5201/7813], Actor Loss: 0.1211, Critic Loss: 0.0595, Reward: 4.1177\n",
      "Validation average reward after epoch 1: 4.0671\n",
      "Epoch [1/20], Batch [5301/7813], Actor Loss: -0.2357, Critic Loss: 0.0720, Reward: 4.1248\n",
      "Validation average reward after epoch 1: 4.0695\n",
      "Epoch [1/20], Batch [5401/7813], Actor Loss: -0.3101, Critic Loss: 0.0668, Reward: 4.1521\n",
      "Validation average reward after epoch 1: 4.0682\n",
      "Epoch [1/20], Batch [5501/7813], Actor Loss: -0.3150, Critic Loss: 0.0810, Reward: 4.1693\n",
      "Validation average reward after epoch 1: 4.0882\n",
      "Epoch [1/20], Batch [5601/7813], Actor Loss: 0.2674, Critic Loss: 0.0601, Reward: 4.1293\n",
      "Validation average reward after epoch 1: 4.0815\n",
      "Epoch [1/20], Batch [5701/7813], Actor Loss: 0.0119, Critic Loss: 0.0535, Reward: 4.0509\n",
      "Validation average reward after epoch 1: 4.0730\n",
      "Epoch [1/20], Batch [5801/7813], Actor Loss: -0.1224, Critic Loss: 0.0541, Reward: 4.1411\n",
      "Validation average reward after epoch 1: 4.0560\n",
      "Epoch [1/20], Batch [5901/7813], Actor Loss: -0.2973, Critic Loss: 0.0547, Reward: 4.0940\n",
      "Validation average reward after epoch 1: 4.0694\n",
      "Epoch [1/20], Batch [6001/7813], Actor Loss: -0.2893, Critic Loss: 0.0665, Reward: 4.1281\n",
      "Validation average reward after epoch 1: 4.0653\n",
      "Epoch [1/20], Batch [6101/7813], Actor Loss: -0.0332, Critic Loss: 0.0782, Reward: 4.2032\n",
      "Validation average reward after epoch 1: 4.0833\n",
      "Epoch [1/20], Batch [6201/7813], Actor Loss: -0.3535, Critic Loss: 0.0575, Reward: 4.1065\n",
      "Validation average reward after epoch 1: 4.0756\n",
      "Epoch [1/20], Batch [6301/7813], Actor Loss: -0.7416, Critic Loss: 0.0836, Reward: 4.1719\n",
      "Validation average reward after epoch 1: 4.0545\n",
      "Epoch [1/20], Batch [6401/7813], Actor Loss: -0.2219, Critic Loss: 0.0539, Reward: 4.1430\n",
      "Validation average reward after epoch 1: 4.0694\n",
      "Epoch [1/20], Batch [6501/7813], Actor Loss: -0.2841, Critic Loss: 0.0432, Reward: 4.0671\n",
      "Validation average reward after epoch 1: 4.0501\n",
      "Epoch [1/20], Batch [6601/7813], Actor Loss: -0.6153, Critic Loss: 0.0728, Reward: 4.1902\n",
      "Validation average reward after epoch 1: 4.0485\n",
      "Epoch [1/20], Batch [6701/7813], Actor Loss: -0.0329, Critic Loss: 0.0686, Reward: 4.0953\n",
      "Validation average reward after epoch 1: 4.0642\n",
      "Epoch [1/20], Batch [6801/7813], Actor Loss: -0.0377, Critic Loss: 0.0421, Reward: 4.0492\n",
      "Validation average reward after epoch 1: 4.0458\n",
      "Epoch [1/20], Batch [6901/7813], Actor Loss: 0.5011, Critic Loss: 0.0706, Reward: 4.1312\n",
      "Validation average reward after epoch 1: 4.0689\n",
      "Epoch [1/20], Batch [7001/7813], Actor Loss: -0.2545, Critic Loss: 0.0630, Reward: 4.0840\n",
      "Validation average reward after epoch 1: 4.0577\n",
      "Epoch [1/20], Batch [7101/7813], Actor Loss: -0.0201, Critic Loss: 0.0539, Reward: 4.0532\n",
      "Validation average reward after epoch 1: 4.0544\n",
      "Epoch [1/20], Batch [7201/7813], Actor Loss: -0.0292, Critic Loss: 0.0662, Reward: 4.1001\n",
      "Validation average reward after epoch 1: 4.0475\n",
      "Epoch [1/20], Batch [7301/7813], Actor Loss: -0.0217, Critic Loss: 0.0537, Reward: 4.1431\n",
      "Validation average reward after epoch 1: 4.0528\n",
      "Epoch [1/20], Batch [7401/7813], Actor Loss: 0.0100, Critic Loss: 0.0434, Reward: 4.0781\n",
      "Validation average reward after epoch 1: 4.0470\n",
      "Epoch [1/20], Batch [7501/7813], Actor Loss: 0.1573, Critic Loss: 0.0685, Reward: 4.0433\n",
      "Validation average reward after epoch 1: 4.0386\n",
      "Epoch [1/20], Batch [7601/7813], Actor Loss: -0.3361, Critic Loss: 0.0569, Reward: 4.1104\n",
      "Validation average reward after epoch 1: 4.0707\n",
      "Epoch [1/20], Batch [7701/7813], Actor Loss: -0.5999, Critic Loss: 0.0847, Reward: 4.1329\n",
      "Validation average reward after epoch 1: 4.0462\n",
      "Epoch [1/20], Batch [7801/7813], Actor Loss: 0.1944, Critic Loss: 0.0589, Reward: 4.1117\n",
      "Validation average reward after epoch 1: 4.0410\n",
      "Epoch [2/20], Batch [1/7813], Actor Loss: -0.3894, Critic Loss: 0.0593, Reward: 4.0892\n",
      "Validation average reward after epoch 2: 4.0502\n",
      "Epoch [2/20], Batch [101/7813], Actor Loss: -0.0794, Critic Loss: 0.0505, Reward: 4.1016\n",
      "Validation average reward after epoch 2: 4.0417\n",
      "Epoch [2/20], Batch [201/7813], Actor Loss: 0.0496, Critic Loss: 0.0474, Reward: 4.0846\n",
      "Validation average reward after epoch 2: 4.0602\n",
      "Epoch [2/20], Batch [301/7813], Actor Loss: -0.1615, Critic Loss: 0.0540, Reward: 4.0451\n",
      "Validation average reward after epoch 2: 4.0421\n",
      "Epoch [2/20], Batch [401/7813], Actor Loss: 0.1187, Critic Loss: 0.0443, Reward: 4.0193\n",
      "Validation average reward after epoch 2: 4.0414\n",
      "Epoch [2/20], Batch [501/7813], Actor Loss: -0.5397, Critic Loss: 0.0599, Reward: 4.1119\n",
      "Validation average reward after epoch 2: 4.0355\n",
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      "Validation average reward after epoch 2: 4.0530\n",
      "Epoch [2/20], Batch [701/7813], Actor Loss: -0.1099, Critic Loss: 0.0425, Reward: 4.1208\n",
      "Validation average reward after epoch 2: 4.0411\n",
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      "Validation average reward after epoch 2: 4.0527\n",
      "Epoch [2/20], Batch [901/7813], Actor Loss: 0.1464, Critic Loss: 0.0617, Reward: 4.0471\n",
      "Validation average reward after epoch 2: 4.0490\n",
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      "Validation average reward after epoch 2: 4.0501\n",
      "Epoch [2/20], Batch [1101/7813], Actor Loss: -0.4296, Critic Loss: 0.0596, Reward: 4.1626\n",
      "Validation average reward after epoch 2: 4.0276\n",
      "Epoch [2/20], Batch [1201/7813], Actor Loss: -0.4657, Critic Loss: 0.0720, Reward: 4.0465\n",
      "Validation average reward after epoch 2: 4.0352\n",
      "Epoch [2/20], Batch [1301/7813], Actor Loss: -0.3051, Critic Loss: 0.0521, Reward: 4.0790\n",
      "Validation average reward after epoch 2: 4.0450\n",
      "Epoch [2/20], Batch [1401/7813], Actor Loss: -0.1615, Critic Loss: 0.0508, Reward: 4.0585\n",
      "Validation average reward after epoch 2: 4.0264\n",
      "Epoch [2/20], Batch [1501/7813], Actor Loss: -0.1236, Critic Loss: 0.0469, Reward: 4.0384\n",
      "Validation average reward after epoch 2: 4.0353\n",
      "Epoch [2/20], Batch [1601/7813], Actor Loss: -0.4629, Critic Loss: 0.0468, Reward: 4.0559\n",
      "Validation average reward after epoch 2: 4.0215\n",
      "Epoch [2/20], Batch [1701/7813], Actor Loss: -0.1437, Critic Loss: 0.0511, Reward: 4.1088\n",
      "Validation average reward after epoch 2: 4.0211\n",
      "Epoch [2/20], Batch [1801/7813], Actor Loss: -0.3463, Critic Loss: 0.0517, Reward: 4.1135\n",
      "Validation average reward after epoch 2: 4.0353\n",
      "Epoch [2/20], Batch [1901/7813], Actor Loss: -0.1118, Critic Loss: 0.0584, Reward: 4.1062\n",
      "Validation average reward after epoch 2: 4.0368\n",
      "Epoch [2/20], Batch [2001/7813], Actor Loss: -0.1028, Critic Loss: 0.0450, Reward: 4.1091\n",
      "Validation average reward after epoch 2: 4.0253\n",
      "Epoch [2/20], Batch [2101/7813], Actor Loss: -0.3059, Critic Loss: 0.0486, Reward: 4.0661\n",
      "Validation average reward after epoch 2: 4.0408\n",
      "Epoch [2/20], Batch [2201/7813], Actor Loss: -0.1623, Critic Loss: 0.0674, Reward: 4.1294\n",
      "Validation average reward after epoch 2: 4.0672\n",
      "Epoch [2/20], Batch [2301/7813], Actor Loss: -0.1425, Critic Loss: 0.0380, Reward: 4.0518\n",
      "Validation average reward after epoch 2: 4.0406\n",
      "Epoch [2/20], Batch [2401/7813], Actor Loss: -0.0634, Critic Loss: 0.0449, Reward: 4.1148\n",
      "Validation average reward after epoch 2: 4.0421\n",
      "Epoch [2/20], Batch [2501/7813], Actor Loss: -0.1157, Critic Loss: 0.0507, Reward: 4.0746\n",
      "Validation average reward after epoch 2: 4.0412\n",
      "Epoch [2/20], Batch [2601/7813], Actor Loss: -0.0311, Critic Loss: 0.0416, Reward: 4.1032\n",
      "Validation average reward after epoch 2: 4.0474\n",
      "Epoch [2/20], Batch [2701/7813], Actor Loss: -0.1454, Critic Loss: 0.0586, Reward: 4.0728\n",
      "Validation average reward after epoch 2: 4.0308\n",
      "Epoch [2/20], Batch [2801/7813], Actor Loss: -0.0802, Critic Loss: 0.0430, Reward: 4.0594\n",
      "Validation average reward after epoch 2: 4.0384\n",
      "Epoch [2/20], Batch [2901/7813], Actor Loss: -0.4178, Critic Loss: 0.0640, Reward: 4.1387\n",
      "Validation average reward after epoch 2: 4.0281\n",
      "Epoch [2/20], Batch [3001/7813], Actor Loss: -0.2725, Critic Loss: 0.0504, Reward: 4.0430\n",
      "Validation average reward after epoch 2: 4.0271\n",
      "Epoch [2/20], Batch [3101/7813], Actor Loss: -0.1526, Critic Loss: 0.0492, Reward: 3.9805\n",
      "Validation average reward after epoch 2: 4.0318\n",
      "Epoch [2/20], Batch [3201/7813], Actor Loss: -0.2226, Critic Loss: 0.0500, Reward: 4.0696\n",
      "Validation average reward after epoch 2: 4.0299\n",
      "Epoch [2/20], Batch [3301/7813], Actor Loss: -0.2066, Critic Loss: 0.0461, Reward: 3.9866\n",
      "Validation average reward after epoch 2: 4.0197\n",
      "Epoch [2/20], Batch [3401/7813], Actor Loss: -0.0752, Critic Loss: 0.0471, Reward: 4.0256\n",
      "Validation average reward after epoch 2: 4.0248\n",
      "Epoch [2/20], Batch [3501/7813], Actor Loss: 0.0183, Critic Loss: 0.0445, Reward: 4.0281\n",
      "Validation average reward after epoch 2: 4.0234\n",
      "Epoch [2/20], Batch [3601/7813], Actor Loss: -0.0825, Critic Loss: 0.0480, Reward: 4.1693\n",
      "Validation average reward after epoch 2: 4.0274\n",
      "Epoch [2/20], Batch [3701/7813], Actor Loss: 0.0297, Critic Loss: 0.0374, Reward: 4.0715\n",
      "Validation average reward after epoch 2: 4.0253\n",
      "Epoch [2/20], Batch [3801/7813], Actor Loss: -0.2521, Critic Loss: 0.0515, Reward: 4.0836\n",
      "Validation average reward after epoch 2: 4.0289\n",
      "Epoch [2/20], Batch [3901/7813], Actor Loss: 0.0060, Critic Loss: 0.0437, Reward: 4.0295\n",
      "Validation average reward after epoch 2: 4.0186\n",
      "Epoch [2/20], Batch [4001/7813], Actor Loss: -0.0179, Critic Loss: 0.0450, Reward: 4.0542\n",
      "Validation average reward after epoch 2: 4.0188\n",
      "Epoch [2/20], Batch [4101/7813], Actor Loss: -0.2258, Critic Loss: 0.0469, Reward: 4.1219\n",
      "Validation average reward after epoch 2: 4.0241\n",
      "Epoch [2/20], Batch [4201/7813], Actor Loss: -0.1479, Critic Loss: 0.0487, Reward: 4.0822\n",
      "Validation average reward after epoch 2: 4.0306\n",
      "Epoch [2/20], Batch [4301/7813], Actor Loss: -0.2612, Critic Loss: 0.0463, Reward: 4.0690\n",
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      "Validation average reward after epoch 19: 4.0147\n",
      "Epoch [19/20], Batch [6701/7813], Actor Loss: -0.0103, Critic Loss: 0.0244, Reward: 3.9950\n",
      "Validation average reward after epoch 19: 4.0191\n",
      "Epoch [19/20], Batch [6801/7813], Actor Loss: -0.0078, Critic Loss: 0.0264, Reward: 4.0189\n",
      "Validation average reward after epoch 19: 4.0309\n",
      "Epoch [19/20], Batch [6901/7813], Actor Loss: -0.0115, Critic Loss: 0.0274, Reward: 4.0222\n",
      "Validation average reward after epoch 19: 4.0368\n",
      "Epoch [19/20], Batch [7001/7813], Actor Loss: -0.0046, Critic Loss: 0.0278, Reward: 3.9910\n",
      "Validation average reward after epoch 19: 4.0400\n",
      "Epoch [19/20], Batch [7101/7813], Actor Loss: -0.0039, Critic Loss: 0.0227, Reward: 4.0304\n",
      "Validation average reward after epoch 19: 4.0109\n",
      "Epoch [19/20], Batch [7201/7813], Actor Loss: -0.0030, Critic Loss: 0.0206, Reward: 4.0646\n",
      "Validation average reward after epoch 19: 4.0117\n",
      "Epoch [19/20], Batch [7301/7813], Actor Loss: -0.0144, Critic Loss: 0.0225, Reward: 3.9887\n",
      "Validation average reward after epoch 19: 4.0054\n",
      "Epoch [19/20], Batch [7401/7813], Actor Loss: -0.0187, Critic Loss: 0.0238, Reward: 3.9621\n",
      "Validation average reward after epoch 19: 4.0119\n",
      "Epoch [19/20], Batch [7501/7813], Actor Loss: -0.0052, Critic Loss: 0.0197, Reward: 4.0176\n",
      "Validation average reward after epoch 19: 4.0157\n",
      "Epoch [19/20], Batch [7601/7813], Actor Loss: -0.0113, Critic Loss: 0.0391, Reward: 4.0134\n",
      "Validation average reward after epoch 19: 4.0150\n",
      "Epoch [19/20], Batch [7701/7813], Actor Loss: -0.0050, Critic Loss: 0.0249, Reward: 4.0239\n",
      "Validation average reward after epoch 19: 4.0210\n",
      "Epoch [19/20], Batch [7801/7813], Actor Loss: -0.0188, Critic Loss: 0.0214, Reward: 4.0335\n",
      "Validation average reward after epoch 19: 4.0214\n",
      "Epoch [20/20], Batch [1/7813], Actor Loss: -0.0014, Critic Loss: 0.0246, Reward: 3.9854\n",
      "Validation average reward after epoch 20: 4.0253\n",
      "Epoch [20/20], Batch [101/7813], Actor Loss: 0.0007, Critic Loss: 0.0240, Reward: 4.0203\n",
      "Validation average reward after epoch 20: 4.0382\n",
      "Epoch [20/20], Batch [201/7813], Actor Loss: 0.0006, Critic Loss: 0.0304, Reward: 4.0159\n",
      "Validation average reward after epoch 20: 4.0210\n",
      "Epoch [20/20], Batch [301/7813], Actor Loss: -0.0057, Critic Loss: 0.0244, Reward: 3.9775\n",
      "Validation average reward after epoch 20: 4.0178\n",
      "Epoch [20/20], Batch [401/7813], Actor Loss: -0.0033, Critic Loss: 0.0281, Reward: 4.0380\n",
      "Validation average reward after epoch 20: 4.0464\n",
      "Epoch [20/20], Batch [501/7813], Actor Loss: 0.0042, Critic Loss: 0.0240, Reward: 4.0538\n",
      "Validation average reward after epoch 20: 4.0184\n",
      "Epoch [20/20], Batch [601/7813], Actor Loss: 0.0065, Critic Loss: 0.0218, Reward: 4.0502\n",
      "Validation average reward after epoch 20: 4.0317\n",
      "Epoch [20/20], Batch [701/7813], Actor Loss: -0.0046, Critic Loss: 0.0241, Reward: 4.0897\n",
      "Validation average reward after epoch 20: 4.0475\n",
      "Epoch [20/20], Batch [801/7813], Actor Loss: 0.0027, Critic Loss: 0.0294, Reward: 3.9708\n",
      "Validation average reward after epoch 20: 4.0354\n",
      "Epoch [20/20], Batch [901/7813], Actor Loss: 0.0037, Critic Loss: 0.0226, Reward: 3.9551\n",
      "Validation average reward after epoch 20: 4.0260\n",
      "Epoch [20/20], Batch [1001/7813], Actor Loss: -0.0055, Critic Loss: 0.0256, Reward: 4.0440\n",
      "Validation average reward after epoch 20: 4.0426\n",
      "Epoch [20/20], Batch [1101/7813], Actor Loss: -0.0050, Critic Loss: 0.0238, Reward: 4.0381\n",
      "Validation average reward after epoch 20: 4.0321\n",
      "Epoch [20/20], Batch [1201/7813], Actor Loss: 0.0037, Critic Loss: 0.0234, Reward: 4.0732\n",
      "Validation average reward after epoch 20: 4.0392\n",
      "Epoch [20/20], Batch [1301/7813], Actor Loss: 0.0130, Critic Loss: 0.0304, Reward: 4.0136\n",
      "Validation average reward after epoch 20: 4.0418\n",
      "Epoch [20/20], Batch [1401/7813], Actor Loss: -0.0110, Critic Loss: 0.0441, Reward: 4.1180\n",
      "Validation average reward after epoch 20: 4.0438\n",
      "Epoch [20/20], Batch [1501/7813], Actor Loss: 0.0003, Critic Loss: 0.0337, Reward: 4.0496\n",
      "Validation average reward after epoch 20: 4.0643\n",
      "Epoch [20/20], Batch [1601/7813], Actor Loss: 0.0110, Critic Loss: 0.0375, Reward: 4.0804\n",
      "Validation average reward after epoch 20: 4.0489\n",
      "Epoch [20/20], Batch [1701/7813], Actor Loss: -0.0019, Critic Loss: 0.0307, Reward: 4.0638\n",
      "Validation average reward after epoch 20: 4.0371\n",
      "Epoch [20/20], Batch [1801/7813], Actor Loss: -0.0109, Critic Loss: 0.0269, Reward: 3.9550\n",
      "Validation average reward after epoch 20: 4.0182\n",
      "Epoch [20/20], Batch [1901/7813], Actor Loss: 0.0001, Critic Loss: 0.0246, Reward: 4.0027\n",
      "Validation average reward after epoch 20: 4.0269\n",
      "Epoch [20/20], Batch [2001/7813], Actor Loss: -0.0068, Critic Loss: 0.0436, Reward: 4.0072\n",
      "Validation average reward after epoch 20: 4.0548\n",
      "Epoch [20/20], Batch [2101/7813], Actor Loss: 0.0041, Critic Loss: 0.0459, Reward: 4.1024\n",
      "Validation average reward after epoch 20: 4.0637\n",
      "Epoch [20/20], Batch [2201/7813], Actor Loss: 0.0001, Critic Loss: 0.0262, Reward: 4.0966\n",
      "Validation average reward after epoch 20: 4.0344\n",
      "Epoch [20/20], Batch [2301/7813], Actor Loss: 0.0013, Critic Loss: 0.0324, Reward: 4.0127\n",
      "Validation average reward after epoch 20: 4.0474\n",
      "Epoch [20/20], Batch [2401/7813], Actor Loss: 0.0019, Critic Loss: 0.0271, Reward: 3.9608\n",
      "Validation average reward after epoch 20: 4.0484\n",
      "Epoch [20/20], Batch [2501/7813], Actor Loss: -0.0164, Critic Loss: 0.0319, Reward: 4.0768\n",
      "Validation average reward after epoch 20: 4.0525\n",
      "Epoch [20/20], Batch [2601/7813], Actor Loss: -0.0078, Critic Loss: 0.0240, Reward: 4.0637\n",
      "Validation average reward after epoch 20: 4.0322\n",
      "Epoch [20/20], Batch [2701/7813], Actor Loss: -0.0066, Critic Loss: 0.0260, Reward: 4.0896\n",
      "Validation average reward after epoch 20: 4.0314\n",
      "Epoch [20/20], Batch [2801/7813], Actor Loss: -0.0125, Critic Loss: 0.0326, Reward: 4.0689\n",
      "Validation average reward after epoch 20: 4.0425\n",
      "Epoch [20/20], Batch [2901/7813], Actor Loss: -0.0057, Critic Loss: 0.0310, Reward: 4.0483\n",
      "Validation average reward after epoch 20: 4.0396\n",
      "Epoch [20/20], Batch [3001/7813], Actor Loss: -0.0050, Critic Loss: 0.0332, Reward: 4.0359\n",
      "Validation average reward after epoch 20: 4.0516\n",
      "Epoch [20/20], Batch [3101/7813], Actor Loss: -0.0185, Critic Loss: 0.0339, Reward: 3.9973\n",
      "Validation average reward after epoch 20: 4.0441\n",
      "Epoch [20/20], Batch [3201/7813], Actor Loss: -0.0018, Critic Loss: 0.0241, Reward: 4.0731\n",
      "Validation average reward after epoch 20: 4.0388\n",
      "Epoch [20/20], Batch [3301/7813], Actor Loss: -0.0135, Critic Loss: 0.0258, Reward: 4.0695\n",
      "Validation average reward after epoch 20: 4.0422\n",
      "Epoch [20/20], Batch [3401/7813], Actor Loss: -0.0199, Critic Loss: 0.0276, Reward: 4.0396\n",
      "Validation average reward after epoch 20: 4.0630\n",
      "Epoch [20/20], Batch [3501/7813], Actor Loss: -0.0037, Critic Loss: 0.0265, Reward: 4.0700\n",
      "Validation average reward after epoch 20: 4.0386\n",
      "Epoch [20/20], Batch [3601/7813], Actor Loss: -0.0136, Critic Loss: 0.0306, Reward: 4.0012\n",
      "Validation average reward after epoch 20: 4.0275\n",
      "Epoch [20/20], Batch [3701/7813], Actor Loss: -0.0013, Critic Loss: 0.0281, Reward: 4.0244\n",
      "Validation average reward after epoch 20: 4.0344\n",
      "Epoch [20/20], Batch [3801/7813], Actor Loss: -0.0009, Critic Loss: 0.0301, Reward: 4.0217\n",
      "Validation average reward after epoch 20: 4.0653\n",
      "Epoch [20/20], Batch [3901/7813], Actor Loss: -0.0023, Critic Loss: 0.0317, Reward: 4.0334\n",
      "Validation average reward after epoch 20: 4.0441\n",
      "Epoch [20/20], Batch [4001/7813], Actor Loss: 0.0046, Critic Loss: 0.0230, Reward: 4.0104\n",
      "Validation average reward after epoch 20: 4.0383\n",
      "Epoch [20/20], Batch [4101/7813], Actor Loss: 0.0029, Critic Loss: 0.0232, Reward: 4.0078\n",
      "Validation average reward after epoch 20: 4.0417\n",
      "Epoch [20/20], Batch [4201/7813], Actor Loss: -0.0045, Critic Loss: 0.0294, Reward: 4.0247\n",
      "Validation average reward after epoch 20: 4.0616\n",
      "Epoch [20/20], Batch [4301/7813], Actor Loss: -0.0099, Critic Loss: 0.0341, Reward: 4.0045\n",
      "Validation average reward after epoch 20: 4.0402\n",
      "Epoch [20/20], Batch [4401/7813], Actor Loss: -0.0183, Critic Loss: 0.0266, Reward: 4.0631\n",
      "Validation average reward after epoch 20: 4.0351\n",
      "Epoch [20/20], Batch [4501/7813], Actor Loss: -0.0069, Critic Loss: 0.0289, Reward: 4.0599\n",
      "Validation average reward after epoch 20: 4.0307\n",
      "Epoch [20/20], Batch [4601/7813], Actor Loss: -0.0340, Critic Loss: 0.0270, Reward: 4.0871\n",
      "Validation average reward after epoch 20: 4.0447\n",
      "Epoch [20/20], Batch [4701/7813], Actor Loss: -0.0054, Critic Loss: 0.0239, Reward: 4.0836\n",
      "Validation average reward after epoch 20: 4.0421\n",
      "Epoch [20/20], Batch [4801/7813], Actor Loss: -0.0012, Critic Loss: 0.0248, Reward: 4.1270\n",
      "Validation average reward after epoch 20: 4.0590\n",
      "Epoch [20/20], Batch [4901/7813], Actor Loss: -0.0014, Critic Loss: 0.0304, Reward: 4.0154\n",
      "Validation average reward after epoch 20: 4.0442\n",
      "Epoch [20/20], Batch [5001/7813], Actor Loss: -0.0008, Critic Loss: 0.0263, Reward: 4.0855\n",
      "Validation average reward after epoch 20: 4.0474\n",
      "Epoch [20/20], Batch [5101/7813], Actor Loss: -0.0072, Critic Loss: 0.0265, Reward: 4.0229\n",
      "Validation average reward after epoch 20: 4.0330\n",
      "Epoch [20/20], Batch [5201/7813], Actor Loss: -0.0034, Critic Loss: 0.0245, Reward: 4.0179\n",
      "Validation average reward after epoch 20: 4.0397\n",
      "Epoch [20/20], Batch [5301/7813], Actor Loss: -0.0083, Critic Loss: 0.0316, Reward: 4.0412\n",
      "Validation average reward after epoch 20: 4.0527\n",
      "Epoch [20/20], Batch [5401/7813], Actor Loss: 0.0015, Critic Loss: 0.0265, Reward: 4.0622\n",
      "Validation average reward after epoch 20: 4.0455\n",
      "Epoch [20/20], Batch [5501/7813], Actor Loss: -0.0062, Critic Loss: 0.0249, Reward: 4.0391\n",
      "Validation average reward after epoch 20: 4.0379\n",
      "Epoch [20/20], Batch [5601/7813], Actor Loss: 0.0137, Critic Loss: 0.0274, Reward: 4.0280\n",
      "Validation average reward after epoch 20: 4.0636\n",
      "Epoch [20/20], Batch [5701/7813], Actor Loss: -0.0104, Critic Loss: 0.0303, Reward: 4.1535\n",
      "Validation average reward after epoch 20: 4.0761\n",
      "Epoch [20/20], Batch [5801/7813], Actor Loss: -0.0075, Critic Loss: 0.0311, Reward: 4.0602\n",
      "Validation average reward after epoch 20: 4.0546\n",
      "Epoch [20/20], Batch [5901/7813], Actor Loss: -0.0085, Critic Loss: 0.0393, Reward: 4.0589\n",
      "Validation average reward after epoch 20: 4.0416\n",
      "Epoch [20/20], Batch [6001/7813], Actor Loss: -0.0026, Critic Loss: 0.0289, Reward: 4.0344\n",
      "Validation average reward after epoch 20: 4.0705\n",
      "Epoch [20/20], Batch [6101/7813], Actor Loss: -0.0095, Critic Loss: 0.0315, Reward: 4.0885\n",
      "Validation average reward after epoch 20: 4.0686\n",
      "Epoch [20/20], Batch [6201/7813], Actor Loss: -0.0292, Critic Loss: 0.0451, Reward: 4.0989\n",
      "Validation average reward after epoch 20: 4.0582\n",
      "Epoch [20/20], Batch [6301/7813], Actor Loss: 0.0107, Critic Loss: 0.0311, Reward: 4.0972\n",
      "Validation average reward after epoch 20: 4.0561\n",
      "Epoch [20/20], Batch [6401/7813], Actor Loss: 0.0002, Critic Loss: 0.0343, Reward: 4.0925\n",
      "Validation average reward after epoch 20: 4.0680\n",
      "Epoch [20/20], Batch [6501/7813], Actor Loss: -0.0024, Critic Loss: 0.0295, Reward: 4.0567\n",
      "Validation average reward after epoch 20: 4.0534\n",
      "Epoch [20/20], Batch [6601/7813], Actor Loss: -0.0046, Critic Loss: 0.0325, Reward: 4.0287\n",
      "Validation average reward after epoch 20: 4.0551\n",
      "Epoch [20/20], Batch [6701/7813], Actor Loss: 0.0030, Critic Loss: 0.0293, Reward: 4.0254\n",
      "Validation average reward after epoch 20: 4.0528\n",
      "Epoch [20/20], Batch [6801/7813], Actor Loss: -0.0068, Critic Loss: 0.0317, Reward: 4.0789\n",
      "Validation average reward after epoch 20: 4.0618\n",
      "Epoch [20/20], Batch [6901/7813], Actor Loss: 0.0003, Critic Loss: 0.0326, Reward: 4.0590\n",
      "Validation average reward after epoch 20: 4.0705\n",
      "Epoch [20/20], Batch [7001/7813], Actor Loss: -0.0031, Critic Loss: 0.0277, Reward: 4.0320\n",
      "Validation average reward after epoch 20: 4.0528\n",
      "Epoch [20/20], Batch [7101/7813], Actor Loss: 0.0151, Critic Loss: 0.0263, Reward: 3.9761\n",
      "Validation average reward after epoch 20: 4.0394\n",
      "Epoch [20/20], Batch [7201/7813], Actor Loss: 0.0032, Critic Loss: 0.0232, Reward: 4.0427\n",
      "Validation average reward after epoch 20: 4.0411\n",
      "Epoch [20/20], Batch [7301/7813], Actor Loss: -0.0015, Critic Loss: 0.0262, Reward: 4.0194\n",
      "Validation average reward after epoch 20: 4.0517\n",
      "Epoch [20/20], Batch [7401/7813], Actor Loss: -0.0051, Critic Loss: 0.0290, Reward: 4.1112\n",
      "Validation average reward after epoch 20: 4.0472\n",
      "Epoch [20/20], Batch [7501/7813], Actor Loss: -0.0000, Critic Loss: 0.0276, Reward: 3.9950\n",
      "Validation average reward after epoch 20: 4.0531\n",
      "Epoch [20/20], Batch [7601/7813], Actor Loss: -0.0010, Critic Loss: 0.0321, Reward: 4.0068\n",
      "Validation average reward after epoch 20: 4.0389\n",
      "Epoch [20/20], Batch [7701/7813], Actor Loss: -0.0006, Critic Loss: 0.0276, Reward: 4.0991\n",
      "Validation average reward after epoch 20: 4.0292\n",
      "Epoch [20/20], Batch [7801/7813], Actor Loss: -0.0090, Critic Loss: 0.0326, Reward: 4.0353\n",
      "Validation average reward after epoch 20: 4.0451\n",
      "Training completed!\n"
     ]
    }
   ],
   "source": [
    "print('Training started...')\n",
    "train(args, train_dataset, val_dataset)\n",
    "print('Training completed!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7099c9b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test started...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test average reward (greedy): 3.9650\n",
      "Test completed!\n"
     ]
    }
   ],
   "source": [
    "print('Test started...')\n",
    "test1(args, test_dataset)\n",
    "print('Test completed!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "49994658",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试 (sampling)\n",
    "def sampling(actor, inputs, num_samples, sub_batch_size=1280):\n",
    "    # inputs: (batch_size, seq_len, input_size)\n",
    "    batch_size = inputs.size(0)\n",
    "    best_idxs, best_rewards = [], []\n",
    "    \n",
    "    for i in range(batch_size):\n",
    "        temp_input = inputs[i].unsqueeze(0)\n",
    "        best_idx, best_reward = None, float('inf')\n",
    "        \n",
    "        num_sub_batches = (num_samples + sub_batch_size - 1) // sub_batch_size\n",
    "        \n",
    "        remaining_samples = num_samples\n",
    "        for _ in range(num_sub_batches):\n",
    "            current_batch_size = min(sub_batch_size, remaining_samples)\n",
    "            remaining_samples -= current_batch_size\n",
    "            \n",
    "            temp_inputs = temp_input.repeat(current_batch_size, 1, 1)\n",
    "            temp_rewards, temp_idxs, _ = actor(temp_inputs)\n",
    "            \n",
    "            sub_best_reward, sub_best_idx = temp_rewards.min(0)\n",
    "            sub_best_reward = sub_best_reward.item()\n",
    "            sub_best_idx = temp_idxs[sub_best_idx]\n",
    "            \n",
    "            if sub_best_reward < best_reward:\n",
    "                best_idx = sub_best_idx\n",
    "                best_reward = sub_best_reward\n",
    "            \n",
    "        best_idxs.append(best_idx)\n",
    "        best_rewards.append(best_reward)\n",
    "    \n",
    "    return torch.tensor(best_rewards, device=inputs.device), torch.stack(best_idxs, dim=0).to(inputs.device)\n",
    "        \n",
    "\n",
    "def test2(args, test_dataset):\n",
    "    test_loader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False)\n",
    "    \n",
    "    actor = Actor(args.embedding_size, args.hidden_size, args.seq_len, args.n_glimpses, True, args.T, reward).to(args.device)\n",
    "    actor.load_state_dict(torch.load(os.path.join(args.output_dir, args.name)))\n",
    "    \n",
    "    actor.eval()\n",
    "    total_rewards, total_nums = 0, 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for i, test_batch in enumerate(test_loader):\n",
    "            inputs = test_batch.to(args.device)\n",
    "            actor_rewards, idxs = sampling(actor, inputs, args.num_samples)\n",
    "            total_rewards += actor_rewards.sum().item()\n",
    "            total_nums += inputs.size(0)\n",
    "            \n",
    "            if i == 0 and inputs.size(0) >= 8:\n",
    "                plot_tsp_paths(args, inputs[:8], idxs[:8], actor_rewards[:8])\n",
    "            \n",
    "    avg_reward = total_rewards / total_nums\n",
    "    print(f\"Test average reward (sampling): {avg_reward:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4fa2fce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test started...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test average reward (sampling): 3.9155\n",
      "Test completed!\n"
     ]
    }
   ],
   "source": [
    "print('Test started...')\n",
    "test2(args, test_dataset)\n",
    "print('Test completed!')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}