class DQN:
def __init__(self, state_dim, action_dim, cfg):
self.action_dim = action_dim
self.state_dim = state_dim
self.loss = 0
self.gamma = cfg.gamma
self.frame_idx = 0 # 用于epsilon的衰减计数
self.epsilon = lambda frame_idx: cfg.epsilon_end + (cfg.epsilon_start - cfg.epsilon_end) * math.exp(-1. * frame_idx / cfg.epsilon_decay)
self.batch_size = cfg.batch_size
self.device = cfg.device
self.policy_net = MLP(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
self.target_net = MLP(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
self.memory = ReplayBuffer(cfg.memory_capacity)
def choose_action(self, state):
'''policy_net负责与环境进行互动并产生相关动作存放到经验池中，因为后边会采样经验池中的数据来重新生成相关Q值，所以此处不进行梯度的更新'''
self.frame_idx += 1
if random.random() > self.epsilon(self.frame_idx):
with torch.no_grad(): # 使用该语句，使policy_net网络不会进行更新
state = torch.tensor([state], device=self.device, dtype=torch.float32)
q_value = self.policy_net(state)
action = q_value.max(1)[1].item() # tensor.max(1)[1]返回最大值对应的下标，即action
else:
action = random.randrange(self.action_dim)
return action
def update(self):
if len(self.memory) < self.batch_size:
return
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
''' 转换为Tensor
'''
state_batch = torch.tensor(state_batch, device=self.device, dtype=torch.float)
action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1)
reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float)
next_state_batch = torch.tensor(next_state_batch, device=self.device, dtype=torch.float)
done_batch = torch.tensor(np.float32(done_batch), device=self.device)
# 计算当前(s_t, a)对应的Q值,此处的Q值用来训练，所以要求计算梯度;
# 其实也可以在choose_action时将q值存到经验池中，就可以不同进行下一步的计算了
q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch)
# 计算s_t+1状态下target_net网络的最大值
next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 由target_net输出的值不会参与到梯度的计算中
# 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch)
self.loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))
self.optimizer.zero_grad()
self.loss.backward()
self.optimizer.step()
def save(self, path):
torch.save(self.target_net.state_dict(), path+'DQN_CheckPoint.pth')
def load(self, path):
self.target_net.load_state_dict(torch.load(path+'DQN_CheckPoint.pth'))
class DoubleDQN:
def __init__(self, state_dim, action_dim, cfg):
self.action_dim = action_dim # 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
self.actions_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(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)
# target_net的初始模型参数完全复制policy_net
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval() # 不启用 BatchNormalization 和 Dropout
# 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)
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([state],
device=self.device,
dtype=torch.float32)
# 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
q_value = self.policy_net(state)
# tensor.max(1)返回每行的最大值以及对应的下标，
# 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
def update(self):
if len(self.memory) < self.batch_size:
return
# 从memory中随机采样transition
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
self.batch_size)
### 转为张量 ###
state_batch = torch.tensor(state_batch,
device=self.device,
dtype=torch.float)
action_batch = torch.tensor(action_batch,
device=self.device).unsqueeze(
1) # 例如tensor([[1],...,[0]])
reward_batch = torch.tensor(
reward_batch, device=self.device,
dtype=torch.float) # tensor([1., 1.,...,1])
next_state_batch = torch.tensor(next_state_batch,
device=self.device,
dtype=torch.float)
done_batch = torch.tensor(np.float32(done_batch),
device=self.device).unsqueeze(
1) # 将bool转为float然后转为张量
# 计算当前(s_t,a)对应的Q(s_t, a)
q_values = self.policy_net(state_batch)
next_q_values = self.policy_net(next_state_batch)
# 代入当前选择的action，得到Q(s_t|a=a_t)
q_value = q_values.gather(dim=1, index=action_batch)
'''以下是Nature DQN的q_target计算方式
# 计算所有next states的Q'(s_{t+1})的最大值，Q'为目标网络的q函数
next_q_state_value = self.target_net(
next_state_batch).max(1)[0].detach() # 比如tensor([ 0.0060, -0.0171,...,])
# 计算 q_target
# 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
q_target = reward_batch + self.gamma * next_q_state_value * (1-done_batch[0])
'''
'''以下是Double DQN q_target计算方式，与NatureDQN稍有不同'''
next_target_values = self.target_net(next_state_batch)
# 选出Q(s_t‘, a)对应的action，代入到next_target_values获得target net对应的next_q_value，即Q’(s_t|a=argmax Q(s_t‘, a))
next_target_q_value = next_target_values.gather(
1,
torch.max(next_q_values, 1)[1].unsqueeze(1)).squeeze(1)
q_target = reward_batch + self.gamma * next_target_q_value * (
1 - done_batch[0])
self.loss = nn.MSELoss()(q_value, q_target.unsqueeze(1)) # 计算 均方误差loss
# 优化模型
self.optimizer.zero_grad(
) # zero_grad清除上一步所有旧的gradients from the last step
# loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
self.loss.backward()
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')
def load(self, path):
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)
class HierarchicalDQN:
def __init__(self, state_dim, action_dim, cfg):
self.state_dim = state_dim
self.action_dim = action_dim
self.gamma = cfg.gamma
self.device = cfg.device
self.batch_size = cfg.batch_size
self.frame_idx = 0
self.epsilon = lambda frame_idx: cfg.epsilon_end + (
cfg.epsilon_start - cfg.epsilon_end) * math.exp(-1. * frame_idx /
cfg.epsilon_decay)
self.policy_net = MLP(2 * state_dim, action_dim,
cfg.hidden_dim).to(self.device)
self.meta_policy_net = MLP(state_dim, state_dim,
cfg.hidden_dim).to(self.device)
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
self.meta_optimizer = optim.Adam(self.meta_policy_net.parameters(),
lr=cfg.lr)
self.memory = ReplayBuffer(cfg.memory_capacity)
self.meta_memory = ReplayBuffer(cfg.memory_capacity)
self.loss_numpy = 0
self.meta_loss_numpy = 0
self.losses = []
self.meta_losses = []
def to_onehot(self, x):
oh = np.zeros(self.state_dim)
oh[x - 1] = 1.
return oh
def set_goal(self, state):
if random.random() > self.epsilon(self.frame_idx):
with torch.no_grad():
state = torch.tensor(state,
device=self.device,
dtype=torch.float32).unsqueeze(0)
goal = self.meta_policy_net(state).max(1)[1].item()
else:
goal = random.randrange(self.state_dim)
return goal
def choose_action(self, state):
self.frame_idx += 1
if random.random() > self.epsilon(self.frame_idx):
with torch.no_grad():
state = torch.tensor(state,
device=self.device,
dtype=torch.float32).unsqueeze(0)
q_value = self.policy_net(state)
action = q_value.max(1)[1].item()
else:
action = random.randrange(self.action_dim)
return action
def update(self):
self.update_policy()
self.update_meta()
def update_policy(self):
if self.batch_size > len(self.memory):
return
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
self.batch_size)
state_batch = torch.tensor(state_batch, dtype=torch.float)
action_batch = torch.tensor(action_batch,
dtype=torch.int64).unsqueeze(1)
reward_batch = torch.tensor(reward_batch, dtype=torch.float)
next_state_batch = torch.tensor(next_state_batch, dtype=torch.float)
done_batch = torch.tensor(np.float32(done_batch))
q_values = self.policy_net(state_batch).gather(
dim=1, index=action_batch).squeeze(1)
next_state_values = self.policy_net(next_state_batch).max(
1)[0].detach()
expected_q_values = reward_batch + 0.99 * next_state_values * (
1 - done_batch)
loss = nn.MSELoss()(q_values, expected_q_values)
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters(): # clip防止梯度爆炸
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
self.loss_numpy = loss.detach().numpy()
self.losses.append(self.loss_numpy)
def update_meta(self):
if self.batch_size > len(self.meta_memory):
return
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.meta_memory.sample(
self.batch_size)
state_batch = torch.tensor(state_batch, dtype=torch.float)
action_batch = torch.tensor(action_batch,
dtype=torch.int64).unsqueeze(1)
reward_batch = torch.tensor(reward_batch, dtype=torch.float)
next_state_batch = torch.tensor(next_state_batch, dtype=torch.float)
done_batch = torch.tensor(np.float32(done_batch))
q_values = self.meta_policy_net(state_batch).gather(
dim=1, index=action_batch).squeeze(1)
next_state_values = self.meta_policy_net(next_state_batch).max(
1)[0].detach()
expected_q_values = reward_batch + 0.99 * next_state_values * (
1 - done_batch)
meta_loss = nn.MSELoss()(q_values, expected_q_values)
self.meta_optimizer.zero_grad()
meta_loss.backward()
for param in self.meta_policy_net.parameters(): # clip防止梯度爆炸
param.grad.data.clamp_(-1, 1)
self.meta_optimizer.step()
self.meta_loss_numpy = meta_loss.detach().numpy()
self.meta_losses.append(self.meta_loss_numpy)
def save(self, path):
torch.save(self.policy_net.state_dict(),
path + 'policy_checkpoint.pth')
torch.save(self.meta_policy_net.state_dict(),
path + 'meta_checkpoint.pth')
def load(self, path):
self.policy_net.load_state_dict(
torch.load(path + 'policy_checkpoint.pth'))
self.meta_policy_net.load_state_dict(
torch.load(path + 'meta_checkpoint.pth'))
class DQN:
def __init__(self, state_dim, action_dim, cfg):
self.action_dim = action_dim # 总的动作个数
self.device = cfg.device # 设备，cpu或gpu等
self.gamma = cfg.gamma # 奖励的折扣因子
# e-greedy策略相关参数
self.sample_count = 0 # 用于epsilon的衰减计数
self.epsilon = 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(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)
# target_net的初始模型参数完全复制policy_net
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval() # 不启用 BatchNormalization 和 Dropout
# 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)
def choose_action(self, state, train=True):
'''选择动作
'''
if train:
self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
math.exp(-1. * self.sample_count / self.epsilon_decay)
self.sample_count += 1
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
# 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
state = torch.tensor([state],
device=self.device,
dtype=torch.float32)
# 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
q_value = self.policy_net(state)
# tensor.max(1)返回每行的最大值以及对应的下标，
# 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()
else:
action = random.randrange(self.action_dim)
return action
else:
with torch.no_grad(): # 取消保存梯度
# 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(
[state], device='cpu', dtype=torch.float32
) # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
q_value = self.target_net(state)
# tensor.max(1)返回每行的最大值以及对应的下标，
# 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 update(self):
if len(self.memory) < self.batch_size:
return
# 从memory中随机采样transition
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
self.batch_size)
'''转为张量
例如tensor([[-4.5543e-02, -2.3910e-01, 1.8344e-02, 2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02, 2.3400e-01]])'''
state_batch = torch.tensor(state_batch,
device=self.device,
dtype=torch.float)
action_batch = torch.tensor(action_batch,
device=self.device).unsqueeze(
1) # 例如tensor([[1],...,[0]])
reward_batch = torch.tensor(
reward_batch, device=self.device,
dtype=torch.float) # tensor([1., 1.,...,1])
next_state_batch = torch.tensor(next_state_batch,
device=self.device,
dtype=torch.float)
done_batch = torch.tensor(np.float32(done_batch),
device=self.device).unsqueeze(
1) # 将bool转为float然后转为张量
'''计算当前(s_t,a)对应的Q(s_t, a)'''
'''torch.gather:对于a=torch.Tensor([[1,2],[3,4]]),那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])'''
q_values = self.policy_net(state_batch).gather(
dim=1, index=action_batch) # 等价于self.forward
# 计算所有next states的V(s_{t+1})，即通过target_net中选取reward最大的对应states
next_state_values = self.target_net(next_state_batch).max(
1)[0].detach() # 比如tensor([ 0.0060, -0.0171,...,])
# 计算 expected_q_value
# 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
expected_q_values = reward_batch + self.gamma * \
next_state_values * (1-done_batch[0])
# self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
self.loss = nn.MSELoss()(q_values,
expected_q_values.unsqueeze(1)) # 计算 均方误差loss
# 优化模型
self.optimizer.zero_grad(
) # zero_grad清除上一步所有旧的gradients from the last step
# loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
self.loss.backward()
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 + 'dqn_checkpoint.pth')
def load(self, path):
self.target_net.load_state_dict(torch.load(path +
'dqn_checkpoint.pth'))
class DoubleDQN:
def __init__(self, state_dim, action_dim, cfg):
self.action_dim = action_dim
self.state_dim = state_dim
self.gamma = cfg.gamma
self.policy_net = MLP(state_dim, action_dim,
cfg.hidden_dim).to(cfg.device)
self.target_net = MLP(state_dim, action_dim,
cfg.hidden_dim).to(cfg.device)
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval() # 不启用 BatchNormalization 和 Dropout
self.optim = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
self.device = cfg.device
self.frame_idx = 0
self.epsilon = lambda frame_idx: cfg.epsilon_end + (
cfg.epsilon_start - cfg.epsilon_end) * math.exp(-1. * frame_idx /
cfg.epsilon_decay)
self.memory = ReplayBuffer(cfg.memory_capacity)
self.batch_size = cfg.batch_size
self.loss = 0
def choose_action(self, state):
self.frame_idx += 1
state = torch.tensor([state], device=self.device, dtype=torch.float32)
if random.random() > self.epsilon(self.frame_idx):
with torch.no_grad(): # 此处不进行梯度传播
q_values = self.policy_net(state)
action = q_values.max(1)[1].item()
else:
action = random.randrange(self.action_dim)
return action
def update(self):
if len(self.memory) < self.batch_size:
return
# 抽样数据
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
self.batch_size)
# 将数据转换为Tensor并推送到GPU
state_batch = torch.tensor(state_batch,
device=self.device,
dtype=torch.float32)
action_batch = torch.tensor(action_batch,
device=self.device).unsqueeze(1)
reward_batch = torch.tensor(reward_batch, device=self.device)
next_state_batch = torch.tensor(next_state_batch,
device=self.device,
dtype=torch.float32)
done_batch = torch.tensor(done_batch, device=self.device)
# 产生(s_t,a)下的q值
q_values = self.policy_net(state_batch).gather(dim=1,
index=action_batch)
# 计算next_q_values
next_action_batch = self.policy_net(next_state_batch).max(
1)[1].unsqueeze(1) # 此处就是DoubleDQN的关键，动作的选取是通过policy_net的
next_q_values = self.target_net(next_state_batch).gather(
dim=1,
index=next_action_batch).detach().squeeze(1) # q值是target_net输出的
expected_q_values = reward_batch + self.gamma * next_q_values * (
~done_batch)
self.loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))
self.optim.zero_grad()
self.loss.backward()
for param in self.policy_net.parameters(): # clip防止梯度爆炸
param.grad.data.clamp_(-1, 1)
self.optim.step()
def save(self, path):
torch.save(self.target_net.state_dict(), path + 'DQN_CheckPoint.pth')
def load(self, path):
self.target_net.load_state_dict(torch.load(path +
'DQN_CheckPoint.pth'))
class DQN:
def __init__(self, state_dim, action_dim, cfg):
self.action_dim = action_dim # 总的动作个数
self.device = cfg.device # 设备，cpu或gpu等
self.gamma = cfg.gamma # 奖励的折扣因子
# e-greedy策略相关参数
self.frame_idx = 0 # 用于epsilon的衰减计数
self.epsilon = lambda frame_idx: cfg.epsilon_end + \
(cfg.epsilon_start - cfg.epsilon_end) * \
math.exp(-1. * frame_idx / 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)
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)
self.memory = ReplayBuffer(cfg.memory_capacity)
def choose_action(self, state):
'''选择动作
'''
self.frame_idx += 1
if random.random() > self.epsilon(self.frame_idx):
action = self.predict(state)
else:
action = random.randrange(self.action_dim)
return action
def predict(self,state):
with torch.no_grad():
state = torch.tensor([state], device=self.device, dtype=torch.float32)
q_values = self.policy_net(state)
action = q_values.max(1)[1].item()
return action
def update(self):
if len(self.memory) < self.batch_size:
return
# 从memory中随机采样transition
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
self.batch_size)
'''转为张量
例如tensor([[-4.5543e-02, -2.3910e-01, 1.8344e-02, 2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02, 2.3400e-01]])'''
state_batch = torch.tensor(
state_batch, device=self.device, dtype=torch.float)
action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
1) # 例如tensor([[1],...,[0]])
reward_batch = torch.tensor(
reward_batch, device=self.device, dtype=torch.float) # tensor([1., 1.,...,1])
next_state_batch = torch.tensor(
next_state_batch, device=self.device, dtype=torch.float)
done_batch = torch.tensor(np.float32(
done_batch), device=self.device)
'''计算当前(s_t,a)对应的Q(s_t, a)'''
'''torch.gather:对于a=torch.Tensor([[1,2],[3,4]]),那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])'''
q_values = self.policy_net(state_batch).gather(
dim=1, index=action_batch) # 等价于self.forward
# 计算所有next states的V(s_{t+1})，即通过target_net中选取reward最大的对应states
next_q_values = self.target_net(next_state_batch).max(
1)[0].detach() # 比如tensor([ 0.0060, -0.0171,...,])
# 计算 expected_q_value
# 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
expected_q_values = reward_batch + \
self.gamma * next_q_values * (1-done_batch)
# self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算 均方误差loss
# 优化模型
self.optimizer.zero_grad() # zero_grad清除上一步所有旧的gradients from the last step
# loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
loss.backward()
# 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+'dqn_checkpoint.pth')
def load(self, path):
self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
param.data.copy_(target_param.data)