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JohnJim0816
2021-05-04 15:30:01 +08:00
parent 4b96f5a6b0
commit 747f3238c0
41 changed files with 282 additions and 782 deletions
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28
codes/DoubleDQN/agent.py
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@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:50:49
@LastEditor: John
LastEditTime: 2021-03-28 11:07:35
LastEditTime: 2021-05-04 15:04:45
@Discription:
@Environment: python 3.7.7
'''
@@ -42,15 +42,8 @@ class DoubleDQN:
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
self.loss = 0
self.memory = ReplayBuffer(cfg.memory_capacity)
def choose_action(self, state):
'''选择动作
'''
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
math.exp(-1. * self.actions_count / self.epsilon_decay)
self.actions_count += 1
if random.random() > self.epsilon:
with torch.no_grad():
def predict(self,state):
with torch.no_grad():
# 先转为张量便于丢给神经网络,state元素数据原本为float64
# 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
state = torch.tensor(
@@ -61,6 +54,15 @@ class DoubleDQN:
# 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
# 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()
return action
def choose_action(self, state):
'''选择动作
'''
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
math.exp(-1. * self.actions_count / self.epsilon_decay)
self.actions_count += 1
if random.random() > self.epsilon:
action = self.predict(state)
else:
action = random.randrange(self.action_dim)
return action
@@ -113,7 +115,9 @@ class DoubleDQN:
self.optimizer.step() # 更新模型
def save(self,path):
torch.save(self.target_net.state_dict(), path+'DoubleDQN_checkpoint.pth')
torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
def load(self,path):
self.target_net.load_state_dict(torch.load(path+'DoubleDQN_checkpoint.pth'))
self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
param.data.copy_(target_param.data)