写一个深度强化学习的代码
时间: 2023-07-19 19:26:32 浏览: 43
好的,以下是一个简单的深度强化学习代码示例,用于解决CartPole问题:
```python
import gym
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
# 定义一个深度Q网络
def deep_q_network(input_shape, output_shape):
model = Sequential()
model.add(Dense(32, activation='relu', input_shape=input_shape))
model.add(Dense(32, activation='relu'))
model.add(Dense(output_shape, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=0.001))
return model
# 定义一个经验回放缓冲区
class ReplayBuffer():
def __init__(self, buffer_size):
self.buffer_size = buffer_size
self.buffer = []
def add(self, experience):
self.buffer.append(experience)
if len(self.buffer) > self.buffer_size:
self.buffer.pop(0)
def sample(self, batch_size):
if len(self.buffer) < batch_size:
return None
return np.random.choice(self.buffer, batch_size)
# 定义深度强化学习代理
class DQNAgent():
def __init__(self, state_size, action_size, buffer_size=10000, batch_size=32, gamma=0.95, epsilon=1.0, epsilon_decay=0.995, epsilon_min=0.01):
self.state_size = state_size
self.action_size = action_size
self.memory = ReplayBuffer(buffer_size)
self.batch_size = batch_size
self.gamma = gamma
self.epsilon = epsilon
self.epsilon_decay = epsilon_decay
self.epsilon_min = epsilon_min
self.model = deep_q_network((state_size,), action_size)
# 选择动作
def act(self, state):
if np.random.rand() < self.epsilon:
return np.random.choice(self.action_size)
q_values = self.model.predict(state.reshape(1, -1))[0]
return np.argmax(q_values)
# 记录经验
def remember(self, state, action, reward, next_state, done):
self.memory.add((state, action, reward, next_state, done))
# 训练
def train(self):
batch = self.memory.sample(self.batch_size)
if batch is None:
return
states, actions, rewards, next_states, dones = zip(*batch)
states = np.array(states)
actions = np.array(actions)
rewards = np.array(rewards)
next_states = np.array(next_states)
dones = np.array(dones)
q_values_next = self.model.predict_on_batch(next_states)
q_values_next[dones] = np.zeros(self.action_size)
targets = rewards + self.gamma * np.max(q_values_next, axis=1)
targets_f = self.model.predict_on_batch(states)
targets_f[np.arange(self.batch_size), actions] = targets
self.model.train_on_batch(states, targets_f)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
# 创建CartPole环境
env = gym.make('CartPole-v0')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
# 创建DQN代理
agent = DQNAgent(state_size, action_size)
# 进行训练
num_episodes = 1000
for i in range(num_episodes):
state = env.reset()
done = False
while not done:
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
agent.remember(state, action, reward, next_state, done)
state = next_state
agent.train()
# 模型评估
num_eval_episodes = 10
for i in range(num_eval_episodes):
state = env.reset()
done = False
while not done:
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
state = next_state
env.render()
env.close()
```
这个代码使用了Deep Q Learning算法来解决CartPole问题。它首先定义了一个深度Q网络,然后创建了一个经验回放缓冲区,用于存储智能体的经验。然后,它定义了一个DQNAgent类,该类实现了act,remember和train方法。在训练期间,代理在环境中选择动作,然后将经验存储到缓冲区中。然后,它从缓冲区中取出一批经验,并使用Q学习算法来更新Q网络的权重。最后,它使用训练后的模型来评估CartPole问题。