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johnjim0816
2022-08-15 22:31:37 +08:00
parent cd27cb67b7
commit 73948f1dc8
109 changed files with 3483 additions and 1011 deletions
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projects/codes/DoubleDQN/double_dqn.py
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@@ -63,18 +63,18 @@ class MLP(nn.Module):
return self.fc3(x)
class DoubleDQN:
def __init__(self, n_states, n_actions, cfg):
def __init__(self, n_states, n_actions, model, memory, cfg):
self.n_actions = n_actions # 总的动作个数
self.device = torch.device(cfg.device) # 设备，cpu或gpu等
self.gamma = cfg.gamma
# e-greedy策略相关参数
self.actions_count = 0
self.sample_count = 0
self.epsilon_start = cfg.epsilon_start
self.epsilon_end = cfg.epsilon_end
self.epsilon_decay = cfg.epsilon_decay
self.batch_size = cfg.batch_size
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)
self.policy_net = model.to(self.device)
self.target_net = model.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)
@@ -82,13 +82,13 @@ class DoubleDQN:
# 可查parameters()与state_dict()的区别，前者require_grad=True
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
self.loss = 0
self.memory = ReplayBuffer(cfg.memory_capacity)
self.memory = memory
def choose_action(self, state):
def sample(self, state):
'''选择动作
'''
self.actions_count += 1
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * math.exp(-1. * self.actions_count / self.epsilon_decay)
self.sample_count += 1
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * math.exp(-1. * self.sample_count / self.epsilon_decay)
if random.random() > self.epsilon:
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
@@ -104,9 +104,16 @@ class DoubleDQN:
else:
action = random.randrange(self.n_actions)
return action
def predict(self, state):
'''选择动作
'''
with torch.no_grad():
state = torch.tensor([state], device=self.device, dtype=torch.float32)
q_value = self.policy_net(state)
action = q_value.max(1)[1].item()
return action
def update(self):
if len(self.memory) < self.batch_size:
if len(self.memory) < self.batch_size: # 只有memory满了才会更新
return
# 从memory中随机采样transition
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
@@ -150,7 +157,7 @@ class DoubleDQN:
for param in self.policy_net.parameters(): # clip防止梯度爆炸
param.grad.data.clamp_(-1, 1)
self.optimizer.step() # 更新模型
def save(self,path):
torch.save(self.target_net.state_dict(), path+'checkpoint.pth')