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update rainbowdqn

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This commit is contained in:
johnjim0816
2022-05-31 01:20:58 +08:00
parent cfc0f6492e
commit c7c94468c9
149 changed files with 1866 additions and 1549 deletions
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20
codes/DoubleDQN/double_dqn.py
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@@ -46,15 +46,15 @@ class ReplayBuffer:
return len(self.buffer)
class MLP(nn.Module):
def __init__(self, state_dim,action_dim,hidden_dim=128):
def __init__(self, n_states,n_actions,hidden_dim=128):
""" 初始化q网络，为全连接网络
state_dim: 输入的特征数即环境的状态维度
action_dim: 输出的动作维度
n_states: 输入的特征数即环境的状态维度
n_actions: 输出的动作维度
"""
super(MLP, self).__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim) # 输入层
self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层
self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层
self.fc3 = nn.Linear(hidden_dim, action_dim) # 输出层
self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层
def forward(self, x):
# 各层对应的激活函数
@@ -63,8 +63,8 @@ class MLP(nn.Module):
return self.fc3(x)
class DoubleDQN:
def __init__(self, state_dim, action_dim, cfg):
self.action_dim = action_dim # 总的动作个数
def __init__(self, n_states, n_actions, cfg):
self.n_actions = n_actions # 总的动作个数
self.device = cfg.device # 设备，cpu或gpu等
self.gamma = cfg.gamma
# e-greedy策略相关参数
@@ -73,8 +73,8 @@ class DoubleDQN:
self.epsilon_end = cfg.epsilon_end
self.epsilon_decay = cfg.epsilon_decay
self.batch_size = cfg.batch_size
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)
# target_net copy from policy_net
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
target_param.data.copy_(param.data)
@@ -103,7 +103,7 @@ class DoubleDQN:
# 所以tensor.max(1)[1]返回最大值对应的下标，即action
action = q_value.max(1)[1].item()
else:
action = random.randrange(self.action_dim)
action = random.randrange(self.n_actions)
return action
def update(self):