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johnjim0816
2021-09-16 15:35:40 +08:00
parent 5085040330
commit 34fcebc4b8
31 changed files with 434 additions and 137 deletions
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16
codes/DDPG/agent.py
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@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-09 20:25:52
@LastEditor: John
LastEditTime: 2021-05-04 14:50:17
LastEditTime: 2021-09-16 00:55:30
@Discription:
@Environment: python 3.7.7
'''
@@ -26,7 +26,7 @@ class DDPG:
self.target_critic = Critic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
self.target_actor = Actor(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
# copy parameters to target net
# 复制参数到目标网络
for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
target_param.data.copy_(param.data)
for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
@@ -37,7 +37,7 @@ class DDPG:
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg.actor_lr)
self.memory = ReplayBuffer(cfg.memory_capacity)
self.batch_size = cfg.batch_size
self.soft_tau = cfg.soft_tau
self.soft_tau = cfg.soft_tau # 软更新参数
self.gamma = cfg.gamma
def choose_action(self, state):
@@ -46,11 +46,11 @@ class DDPG:
return action.detach().cpu().numpy()[0, 0]
def update(self):
if len(self.memory) < self.batch_size:
if len(self.memory) < self.batch_size: # 当 memory 中不满足一个批量时，不更新策略
return
state, action, reward, next_state, done = self.memory.sample(
self.batch_size)
# convert variables to Tensor
# 从经验回放中(replay memory)中随机采样一个批量的转移(transition)
state, action, reward, next_state, done = self.memory.sample(self.batch_size)
# 转变为张量
state = torch.FloatTensor(state).to(self.device)
next_state = torch.FloatTensor(next_state).to(self.device)
action = torch.FloatTensor(action).to(self.device)
@@ -70,10 +70,10 @@ class DDPG:
self.actor_optimizer.zero_grad()
policy_loss.backward()
self.actor_optimizer.step()
self.critic_optimizer.zero_grad()
value_loss.backward()
self.critic_optimizer.step()
# 软更新
for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
target_param.data.copy_(
target_param.data * (1.0 - self.soft_tau) +