bacow/easy-rl
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

修复DoubleDQN

Browse Source
This commit is contained in:
johnjim0816
2023-01-14 15:24:43 +08:00
parent 1782eb8f99
commit ab3f84f167
1 changed files with 2 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
notebooks/DoubleDQN.ipynb
View File

@@ -165,7 +165,7 @@
" # 将数据转换为tensor\n", " # 将数据转换为tensor\n",
" state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float)\n", " state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float)\n",
" action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1) \n", " action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1) \n",
" reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float) \n", " reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float).unsqueeze(1) \n",
" next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float)\n", " next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float)\n",
" done_batch = torch.tensor(np.float32(done_batch), device=self.device).unsqueeze(1)\n", " done_batch = torch.tensor(np.float32(done_batch), device=self.device).unsqueeze(1)\n",
" q_value_batch = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 实际的Q值\n", " q_value_batch = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 实际的Q值\n",
@@ -418,21 +418,7 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"开始训练！\n", "开始训练！\n",
"开始更新策略！\n" "开始更新策略！\n",
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\24438\\anaconda3\\envs\\easyrl\\lib\\site-packages\\torch\\nn\\modules\\loss.py:520: UserWarning: Using a target size (torch.Size([64, 64])) that is different to the input size (torch.Size([64, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
" return F.mse_loss(input, target, reduction=self.reduction)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"回合：10/100，奖励：24.00，Epislon：0.663\n", "回合：10/100，奖励：24.00，Epislon：0.663\n",
"回合：20/100，奖励：10.00，Epislon：0.508\n", "回合：20/100，奖励：10.00，Epislon：0.508\n",
"回合：30/100，奖励：10.00，Epislon：0.395\n", "回合：30/100，奖励：10.00，Epislon：0.395\n",