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update codes

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johnjim0816
2021-12-22 16:55:09 +08:00
parent 75df999258
commit 41fb561d25
75 changed files with 1248 additions and 918 deletions
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18
codes/DQN/README.md
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@@ -50,15 +50,15 @@ import torch.nn as nn
import torch.nn.functional as F
class FCN(nn.Module):
def __init__(self, state_dim=4, action_dim=18):
def __init__(self, n_states=4, n_actions=18):
""" 初始化q网络，为全连接网络
state_dim: 输入的feature即环境的state数目
action_dim: 输出的action总个数
n_states: 输入的feature即环境的state数目
n_actions: 输出的action总个数
"""
super(FCN, self).__init__()
self.fc1 = nn.Linear(state_dim, 128) # 输入层
self.fc1 = nn.Linear(n_states, 128) # 输入层
self.fc2 = nn.Linear(128, 128) # 隐藏层
self.fc3 = nn.Linear(128, action_dim) # 输出层
self.fc3 = nn.Linear(128, n_actions) # 输出层
def forward(self, x):
# 各层对应的激活函数
@@ -66,7 +66,7 @@ class FCN(nn.Module):
x = F.relu(self.fc2(x))
return self.fc3(x)
```
输入为state_dim，输出为action_dim，包含一个128维度的隐藏层，这里根据需要可增加隐藏层维度和数量，然后一般使用relu激活函数，这里跟深度学习的网路设置是一样的。
输入为n_states，输出为n_actions，包含一个128维度的隐藏层，这里根据需要可增加隐藏层维度和数量，然后一般使用relu激活函数，这里跟深度学习的网路设置是一样的。
### Replay Buffer
@@ -107,8 +107,8 @@ class ReplayBuffer:
在类中建立两个网络，以及optimizer和memory，
```python
self.policy_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
self.target_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
self.policy_net = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device)
self.target_net = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device)
for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # copy params from policy net
target_param.data.copy_(param.data)
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
@@ -124,7 +124,7 @@ def choose_action(self, state):
if random.random() > self.epsilon(self.frame_idx):
action = self.predict(state)
else:
action = random.randrange(self.action_dim)
action = random.randrange(self.n_actions)
return action
```