gym 0.26.1
CartPole-v1
NoisyNet DQN
NoisyNet就是把原来Linear里的w/b换成mu + sigma * epsilon, 这是一种非常简单的方法，但是可以显著提升DQN的表现。和之前最原始的
DQN相比就是改了两个地方，一个是Linear改成了NoisyLinear,另外一个是在agent在take_action的时候策略 由ε-greedy改成了直接取argmax。详细见下面的代码。
本文的实现参考王树森的深度强化学习。
引用书上的一段话， 噪声
DQN本身就带有随机性,可以鼓励探索,起到与ε-greedy策略相同的作用,直接用a_t = argmax Q(s,a,epsilon; mu,sigma), 作为行为策略，效果比ε-greedy更好。
python
import gym
import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
import random
import collections
from tqdm import tqdm
import matplotlib.pyplot as plt
from d2l import torch as d2l
import rl_utils
import math
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
transition = random.sample(self.buffer, batch_size)
state, action, reward, next_state, done = zip(*transition)
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 NoisyLinear(nn.Linear):
def __init__(self, in_features, out_features, sigma_init=0.017, bias=True):
super().__init__(in_features, out_features, bias)
self.sigma_weight = nn.Parameter(torch.full((out_features, in_features), sigma_init))
self.register_buffer("epsilon_weight", torch.zeros(out_features, in_features))
if bias:
self.sigma_bias = nn.Parameter(torch.full((out_features,), sigma_init))
self.register_buffer("epsilon_bias", torch.zeros(out_features))
self.reset_parameters()
def reset_parameters(self):
std = math.sqrt(3 / self.in_features)
self.weight.data.uniform_(-std, std)
self.bias.data.uniform_(-std, std)
def forward(self, x, is_training=True):
self.epsilon_weight.normal_()
bias = self.bias
if bias is not None:
self.epsilon_bias.normal_()
bias = bias + self.sigma_bias * self.epsilon_bias.data
if is_training:
return F.linear(x, self.weight + self.sigma_weight * self.epsilon_weight.data, bias)
else:
return F.linear(x, self.weight, bias)
class Q(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim):
super().__init__()
self.fc1 = NoisyLinear(state_dim, hidden_dim)
self.fc2 = NoisyLinear(hidden_dim, action_dim)
def forward(self, x, is_training=True):
x = F.relu(self.fc1(x, is_training)) # 隐藏层之后使用ReLU激活函数
return self.fc2(x, is_training)
class DQN:
"""DQN算法"""
def __init__(self, state_dim, hidden_dim, action_dim, lr, gamma, target_update, device):
self.action_dim = action_dim
self.q = Q(state_dim, hidden_dim, action_dim).to(device) # Q网络
self.target_q = Q(state_dim, hidden_dim, action_dim).to(device) # 目标网络
self.target_q.load_state_dict(self.q.state_dict()) # 加载参数
self.optimizer = torch.optim.Adam(self.q.parameters(), lr=lr)
self.gamma = gamma
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): # 这个地方就不用epsilon-贪婪策略
state = torch.tensor(np.array([state]), dtype=torch.float).to(self.device)
action = self.q(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']).reshape(-1,1).to(self.device)
rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).reshape(-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).reshape(-1,1).to(self.device)
q_values = self.q(states).gather(1, actions) # Q值
# 下个状态的最大Q值
max_next_q_values = self.target_q(next_states).max(1)[0].reshape(-1,1)
q_targets = rewards + self.gamma * max_next_q_values * (1- dones) # TD误差
loss = F.mse_loss(q_values, q_targets) # 均方误差
self.optimizer.zero_grad() # 梯度清零,因为默认会梯度累加
loss.mean().backward() # 反向传播
self.optimizer.step() # 更新梯度
if self.count % self.target_update == 0:
self.target_q.load_state_dict(self.q.state_dict())
self.count += 1
python
lr = 2e-3
num_episodes = 500
hidden_dim = 128
gamma = 0.98
target_update = 10
buffer_size = 10000
minimal_size = 500
batch_size = 64
device = d2l.try_gpu()
print(device)
env_name = "CartPole-v1"
env = gym.make(env_name)
random.seed(0)
np.random.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, target_update, device)
return_list = []
for i in range(10):
with tqdm(total=int(num_episodes/10), desc=f'Iteration {i}') as pbar:
for i_episode in range(int(num_episodes/10)):
episode_return = 0
state = env.reset()[0]
done, truncated= False, False
while not done and not truncated :
action = agent.take_action(state)
next_state, reward, done, truncated, info = 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)
episodes_list = list(range(len(return_list)))
plt.plot(episodes_list, return_list)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title(f'Noisy DQN on {env_name}')
plt.show()
mv_return = rl_utils.moving_average(return_list, 9)
plt.plot(episodes_list, mv_return)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title(f'Noisy DQN on {env_name}')
plt.show()这次是在pycharm上运行jupyter file,结果如下:
效果对比之前的DQN 详细参考这篇 表现是显著提升。