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JohnJim0816
2021-03-31 15:37:09 +08:00
parent 6a92f97138
commit b6f63a91bf
65 changed files with 1244 additions and 459 deletions
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codes/DQN/agent.py
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@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:50:49
@LastEditor: John
LastEditTime: 2021-03-13 14:56:23
LastEditTime: 2021-03-30 17:01:26
@Discription:
@Environment: python 3.7.7
'''
@@ -13,6 +13,8 @@ LastEditTime: 2021-03-13 14:56:23
'''
import torch
import torch.nn as nn
import torch.optim as optim
@@ -23,61 +25,44 @@ from common.memory import ReplayBuffer
from common.model import MLP
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
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.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)
# 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.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.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):
def choose_action(self, state):
'''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
self.frame_idx += 1
if random.random() > self.epsilon(self.frame_idx):
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
def update(self):
if len(self.memory) < self.batch_size:
@@ -96,32 +81,31 @@ class DQN:
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
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_state_values = self.target_net(
next_state_batch).max(1)[0].detach() # 比如tensor([ 0.0060, -0.0171,...,])
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_state_values * (1-done_batch[0])
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
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)
# 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):
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'))
def load(self, path):
self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))