gym-0.26.2
cartPole-v1
参考动手学强化学习书中的代码,并做了一些修改
代码
import gym
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
class PolicyNet(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, action_dim)
def forward(self, x):
x = F.relu(self.fc1(x))
return F.softmax(self.fc2(x), dim=1)
class ValueNet(nn.Module):
def __init__(self, state_dim, hidden_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, 1)
def forward(self, x):
x = F.relu(self.fc1(x))
return self.fc2(x)
class PPO:
"""PPO算法，采用截断的方式"""
def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda, epochs, eps, gamma, device):
self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)
self.critic = ValueNet(state_dim, hidden_dim).to(device)
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)
self.gamma = gamma
self.lmbda = lmbda
self.epochs = epochs # 一条序列的数据用来训练轮数
self.eps = eps # PPO 中阶段范围的参数
self.device = device
def take_action(self, state):
state = torch.FloatTensor([state]).to(self.device)
probs = self.actor(state)
action_dist = torch.distributions.Categorical(probs)
action = action_dist.sample()
return action.item()
def gae(self, td_delta):
td_delta = td_delta.detach().numpy()
advantages_list = []
advantage = 0.0
for delta in td_delta[::-1]:
advantage = self.gamma * self.lmbda * advantage + delta
advantages_list.append(advantage)
advantages_list.reverse()
return torch.FloatTensor(advantages_list)
def update(self, transition_dist):
states = torch.FloatTensor(transition_dist['states']).to(self.device)
actions = torch.tensor(transition_dist['actions']).reshape((-1, 1)).to(self.device)
rewards = torch.FloatTensor(transition_dist['rewards']).reshape((-1, 1)).to(self.device)
next_states = torch.FloatTensor(transition_dist['next_states']).to(self.device)
dones = torch.FloatTensor(transition_dist['dones']).reshape((-1, 1)).to(self.device)
td_target = rewards + self.gamma * self.critic(next_states) * (1 - dones)
td_delta = td_target - self.critic(states)
# GAE 计算广义优势
advantage = self.gae(td_delta.cpu()).to(self.device)
old_log_probs = torch.log(self.actor(states).gather(1, actions)).detach()
for _ in range(self.epochs):
log_probs = torch.log(self.actor(states).gather(1, actions))
ration = torch.exp(log_probs - old_log_probs)
surr1 = ration * advantage
surr2 = torch.clamp(ration, 1-self.eps, 1+self.eps) * advantage # 截断
actor_loss = torch.mean(-torch.min(surr1, surr2)) # PPO损失函数
critic_loss = torch.mean(F.mse_loss(self.critic(states), td_target.detach()))
self.actor_optimizer.zero_grad()
self.critic_optimizer.zero_grad()
actor_loss.backward()
critic_loss.backward()
self.actor_optimizer.step()
self.critic_optimizer.step()
def moving_average(a, window_size):
cumulative_sum = np.cumsum(np.insert(a, 0, 0))
middle = (cumulative_sum[window_size:] - cumulative_sum[:-window_size]) / window_size
r = np.arange(1, window_size-1, 2)
begin = np.cumsum(a[:window_size-1])[::2] / r
end = (np.cumsum(a[:-window_size:-1])[::2] / r)[::-1]
return np.concatenate((begin, middle, end))
def train():
actor_lr = 1e-3
critic_lr = 1e-2
num_episodes = 500
hidden_dim = 128
gamma = 0.98
lmbda = 0.95
epochs = 10
eps = 0.2
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
env_name = "CartPole-v1"
env = gym.make(env_name)
torch.manual_seed(0)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda, epochs, eps, gamma, 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
transition_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}
state, _ = env.reset()
done, truncated = False, False
while not done and not truncated:
action = agent.take_action(state)
next_state, reward, done, truncated, _ = env.step(action)
done = done or truncated # 这个地方要注意
transition_dict['states'].append(state)
transition_dict['actions'].append(action)
transition_dict['next_states'].append(next_state)
transition_dict['rewards'].append(reward)
transition_dict['dones'].append(done)
state = next_state
episode_return += reward
return_list.append(episode_return)
agent.update(transition_dict)
if (i_episode + 1) % 10 == 0:
pbar.set_postfix({'episode': '%d' % (num_episodes / 10 * i + i_episode + 1),
服务器托管网'return': '%.3f' % np.mean(return_list[-10:])})
pbar.update(1)
episodes_list = list(range(len(return_list)))
服务器托管网 plt.plot(episodes_list, return_list)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title(f'PPO on {env_name}')
plt.show()
mv_return = moving_average(return_list, 9)
plt.plot(episodes_list, mv_return)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title(f'PPO on {env_name}')
plt.show()
if __name__ == '__main__':
train()
pycharm中运行结果：
效果看起很好。
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