运行动手学强化学习中DQN算法时出现问题，求帮助啊
import random
import gym
import numpy as np
import collections
from tqdm import tqdm
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import rl_utils
class ReplayBuffer:
''' 经验回放池 '''
def __init__(self, capacity):
self.buffer = collections.deque(maxlen=capacity) # 队列,先进先出
def add(self, state, action, reward, next_state, done): # 将数据加入buffer
self.buffer.append((state, action, reward, next_state, done))
def sample(self, batch_size): # 从buffer中采样数据,数量为batch_size
# random.sample(x,size) 随机截取列表x指定size长度，顺序不变
transitions = random.sample(self.buffer, batch_size)
# transitions 包含很多transition，而transition中又包含state, action, reward, next_state, done
# *transitions 是将transition的参数解包出来state, action, reward, next_state, done
# zip(*transitions)是将属于一种属性的封装在一起，如所有state(s1,s2,s3,...)
state, action, reward, next_state, done = zip(*transitions)
return np.array(state), action, reward, np.array(next_state), done
def size(self): # 目前buffer中数据的数量
return len(self.buffer)
class Qnet(torch.nn.Module):
''' 只有一层隐藏层的Q网络 '''
def __init__(self, state_dim, hidden_dim, action_dim):
super(Qnet, self).__init__()
self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
self.fc2 = torch.nn.Linear(hidden_dim, action_dim)
def forward(self, x):
x = F.relu(self.fc1(x)) # 隐藏层使用ReLU激活函数
return self.fc2(x)
class DQN:
''' DQN算法 '''
def __init__(self, state_dim, hidden_dim, action_dim, learning_rate, gamma,
epsilon, target_update, device):
self.action_dim = action_dim
self.q_net = Qnet(state_dim, hidden_dim,
self.action_dim).to(device) # Q网络
# 目标网络
self.target_q_net = Qnet(state_dim, hidden_dim,
self.action_dim).to(device)
# 使用Adam优化器
self.optimizer = torch.optim.Adam(self.q_net.parameters(),
lr=learning_rate)
self.gamma = gamma # 折扣因子
self.epsilon = epsilon # epsilon-贪婪策略
self.target_update = target_update # 目标网络更新频率
self.count = 0 # 计数器,记录更新次数
self.device = device
def take_action(self, state): # epsilon-贪婪策略采取动作
if np.random.random() < self.epsilon:
action = np.random.randint(self.action_dim)
else:
state = torch.tensor([state], dtype=torch.float).to(self.device)
action = self.q_net(state).argmax().item()
return action
def update(self, transition_dict):
states = torch.tensor(transition_dict['states'],
dtype=torch.float).to(self.device)
actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(
self.device)
rewards = torch.tensor(transition_dict['rewards'],
dtype=torch.float).view(-1, 1).to(self.device)
next_states = torch.tensor(transition_dict['next_states'],
dtype=torch.float).to(self.device)
dones = torch.tensor(transition_dict['dones'],
dtype=torch.float).view(-1, 1).to(self.device)
q_values = self.q_net(states).gather(1, actions) # Q值
# 下个状态的最大Q值
max_next_q_values = self.target_q_net(next_states).max(1)[0].view(
-1, 1)
q_targets = rewards + self.gamma * max_next_q_values * (1 - dones
) # TD误差目标
dqn_loss = torch.mean(F.mse_loss(q_values, q_targets)) # 均方误差损失函数
self.optimizer.zero_grad() # PyTorch中默认梯度会累积,这里需要显式将梯度置为0
dqn_loss.backward() # 反向传播更新参数
self.optimizer.step()
if self.count % self.target_update == 0:
self.target_q_net.load_state_dict(
self.q_net.state_dict()) # 更新目标网络
self.count += 1
lr = 2e-3
num_episodes = 500
hidden_dim = 128
gamma = 0.98
epsilon = 0.01
target_update = 10
buffer_size = 10000
minimal_size = 500
batch_size = 64
device = torch.device("cuda") if torch.cuda.is_available() else torch.device(
"cpu")
env_name = 'CartPole-v1'
env = gym.make(env_name)
random.seed(0)
np.random.seed(0)
env.seed(0)
torch.manual_seed(0)
replay_buffer = ReplayBuffer(buffer_size)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
agent = DQN(state_dim, hidden_dim, action_dim, lr, gamma, epsilon,
target_update, device)
return_list = []
for i in range(10):
with tqdm(total=int(num_episodes / 10), desc='Iteration %d' % i) as pbar:
for i_episode in range(int(num_episodes / 10)):
episode_return = 0
state = env.reset()
done = False
while not done:
action = agent.take_action(state)
next_state, reward, done, _ = env.step(action)
replay_buffer.add(state, action, reward, next_state, done)
state = next_state
episode_return += reward
# 当buffer数据的数量超过一定值后,才进行Q网络训练
if replay_buffer.size() > minimal_size:
b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(batch_size)
transition_dict = {
'states': b_s,
'actions': b_a,
'next_states': b_ns,
'rewards': b_r,
'dones': b_d
}
agent.update(transition_dict)
return_list.append(episode_return)
if (i_episode + 1) % 10 == 0:
pbar.set_postfix({
'episode':
'%d' % (num_episodes / 10 * i + i_episode + 1),
'return':
'%.3f' % np.mean(return_list[-10:])
})
pbar.update(1)
运行结果及报错内容
Traceback (most recent call last):
File "E:\graduate student\Python\Reinforcement learning\HANDS-ON Reinforcement learning\04_DQN\01_DQN.py", line 120, in <module>
env.seed(0)
File "D:\Python39\lib\site-packages\gym\core.py", line 241, in __getattr__
return getattr(self.env, name)
File "D:\Python39\lib\site-packages\gym\core.py", line 241, in __getattr__
return getattr(self.env, name)
File "D:\Python39\lib\site-packages\gym\core.py", line 241, in __getattr__
return getattr(self.env, name)
AttributeError: 'CartPoleEnv' object has no attribute 'seed'
