state,reward,done,info = env.step(act[-1])
时间: 2024-04-21 19:22:40 浏览: 18
这段代码是在使用某个环境(`env`)执行一个动作(`act[-1]`)并获得相应的状态(`state`)、奖励(`reward`)、完成标志(`done`)和其他信息(`info`)。
具体而言,这段代码是在执行环境的 `step()` 函数上调用,该函数接受一个动作作为参数,并返回执行该动作后的新状态、奖励、完成标志和其他相关信息。
可以用以下方式使用它:
```
state, reward, done, info = env.step(act[-1])
```
其中,`act[-1]` 是一个动作,可能是一个整数或者一个向量,具体取决于环境的要求。执行该动作后,返回的 `state` 是新的状态;`reward` 是执行动作后获得的奖励;`done` 表示任务是否已经完成,如果完成则为 `True`,否则为 `False`;`info` 是一个字典,包含一些额外的环境相关信息。
通过调用环境的 `step()` 函数并获取返回值,可以实现与环境的交互,进行强化学习等任务的训练和评估。
相关问题
import akshare as ak import numpy as np import pandas as pd import random import matplotlib.pyplot as plt class StockTradingEnv: def __init__(self): self.df = ak.stock_zh_a_daily(symbol='sh000001', adjust="qfq").iloc[::-1] self.observation_space = self.df.shape[1] self.action_space = 3 self.reset() def reset(self): self.current_step = 0 self.total_profit = 0 self.done = False self.state = self.df.iloc[self.current_step].values return self.state def step(self, action): assert self.action_space.contains(action) if action == 0: # 买入 self.buy_stock() elif action == 1: # 卖出 self.sell_stock() else: # 保持不变 pass self.current_step += 1 if self.current_step >= len(self.df) - 1: self.done = True else: self.state = self.df.iloc[self.current_step].values reward = self.get_reward() self.total_profit += reward return self.state, reward, self.done, {} def buy_stock(self): pass def sell_stock(self): pass def get_reward(self): pass class QLearningAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.995 self.learning_rate = 0.1 self.discount_factor = 0.99 self.q_table = np.zeros((self.state_size, self.action_size)) def act(self, state): if np.random.rand() <= self.epsilon: return random.randrange(self.action_size) else: return np.argmax(self.q_table[state, :]) def learn(self, state, action, reward, next_state, done): target = reward + self.discount_factor * np.max(self.q_table[next_state, :]) self.q_table[state, action] = (1 - self.learning_rate) * self.q_table[state, action] + self.learning_rate * target if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay env = StockTradingEnv() agent = QLearningAgent(env.observation_space, env.action_space) for episode in range(1000): state = env.reset() done = False while not done: action = agent.act(state) next_state, reward, done, _ = env.step(action) agent.learn(state, action, reward, next_state, done) state = next_state if episode % 10 == 0: print("Episode: %d, Total Profit: %f" % (episode, env.total_profit)) agent.save_model("model-%d.h5" % episode) def plot_profit(env, title): plt.figure(figsize=(12, 6)) plt.plot(env.df.index, env.df.close, label="Price") plt.plot(env.df.index, env.profits, label="Profits") plt.legend() plt.title(title) plt.show() env = StockTradingEnv() agent = QLearningAgent(env.observation_space, env.action_space) agent.load_model("model-100.h5") state = env.reset() done = False while not done: action = agent.act(state) next_state, reward, done, _ = env.step(action) state = next_state plot_profit(env, "QLearning Trading Strategy")优化代码
1. 对于环境类 `StockTradingEnv`,可以考虑将 `buy_stock` 和 `sell_stock` 方法的具体实现写入 `step` 方法中,避免方法数量过多。
2. 可以将 `get_reward` 方法中的具体实现改为直接计算当前持仓的收益。
3. 在循环训练过程中,可以记录每个 episode 的总收益,并将这些数据保存下来,在训练完成后进行可视化分析。
4. 可以添加更多的参数来控制训练过程,比如学习率、衰减系数等。
5. 可以将 QLearningAgent 类中的方法进行整理和封装,提高代码的可读性和可维护性。同时,也可以添加一些对模型进行保存和加载的方法,便于模型的重用和共享。
多智能体编队actor-critic pytorch代码
对于一个多智能体编队问题,可以采用actor-critic算法来解决。以下是使用PyTorch实现的代码示例:
```
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim):
super(Actor, self).__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, action_dim)
self.relu = nn.ReLU()
self.softmax = nn.Softmax(dim=1)
def forward(self, state):
x = self.relu(self.fc1(state))
x = self.relu(self.fc2(x))
x = self.softmax(self.fc3(x))
return x
class Critic(nn.Module):
def __init__(self, state_dim, hidden_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, 1)
self.relu = nn.ReLU()
def forward(self, state):
x = self.relu(self.fc1(state))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
class Agent():
def __init__(self, state_dim, action_dim, hidden_dim, lr, gamma):
self.actor = Actor(state_dim, action_dim, hidden_dim)
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=lr)
self.critic = Critic(state_dim, hidden_dim)
self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=lr)
self.gamma = gamma
def act(self, state):
state = torch.FloatTensor(state)
action_probs = self.actor(state)
action_probs = action_probs.detach().numpy()
action = np.random.choice(len(action_probs[0]), p=action_probs[0])
return action
def learn(self, states, actions, rewards, next_states, dones):
states = torch.FloatTensor(states)
actions = torch.LongTensor(actions)
rewards = torch.FloatTensor(rewards)
next_states = torch.FloatTensor(next_states)
dones = torch.FloatTensor(dones)
values = self.critic(states).squeeze()
next_values = self.critic(next_states).squeeze()
target_values = rewards + self.gamma * next_values * (1 - dones)
advantages = target_values - values
actor_loss = -torch.log(self.actor(states)[range(len(actions)), actions]) * advantages.detach()
critic_loss = nn.MSELoss()(values, target_values.detach())
self.actor_optimizer.zero_grad()
actor_loss.mean().backward()
self.actor_optimizer.step()
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
if __name__ == '__main__':
env = gym.make('CartPole-v0')
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
hidden_dim = 128
lr = 0.001
gamma = 0.99
agent = Agent(state_dim, action_dim, hidden_dim, lr, gamma)
episodes = 1000
scores = []
for episode in range(episodes):
state = env.reset()
done = False
score = 0
while not done:
action = agent.act(state)
next_state, reward, done, info = env.step(action)
agent.learn(state, action, reward, next_state, done)
state = next_state
score += reward
scores.append(score)
print('Episode: {}, Score: {}'.format(episode, score))
```
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