强化学习代码连续动作
时间: 2023-10-01 16:05:03 浏览: 84
对于强化学习中的连续动作问题,常用的方法是使用深度强化学习算法和确定性策略梯度算法。以下是一个示例代码,演示如何使用Proximal Policy Optimization (PPO)算法来处理连续动作空间。
```python
import gym
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
# 定义Actor模型,输出均值和标准差
class Actor(tf.keras.Model):
def __init__(self, num_actions):
super(Actor, self).__init__()
self.hidden1 = layers.Dense(64, activation='relu')
self.hidden2 = layers.Dense(64, activation='relu')
self.mu = layers.Dense(num_actions, activation='tanh')
self.sigma = layers.Dense(num_actions, activation='softplus')
def call(self, inputs):
x = self.hidden1(inputs)
x = self.hidden2(x)
mu = self.mu(x)
sigma = self.sigma(x)
return mu, sigma
# 定义Critic模型,评估状态值函数
class Critic(tf.keras.Model):
def __init__(self):
super(Critic, self).__init__()
self.hidden1 = layers.Dense(64, activation='relu')
self.hidden2 = layers.Dense(64, activation='relu')
self.value = layers.Dense(1)
def call(self, inputs):
x = self.hidden1(inputs)
x = self.hidden2(x)
value = self.value(x)
return value
# 定义PPO算法的Agent
class PPOAgent:
def __init__(self, num_actions):
self.num_actions = num_actions
self.actor = Actor(num_actions)
self.critic = Critic()
self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
def get_action(self, state):
state = tf.convert_to_tensor([state], dtype=tf.float32)
mu, sigma = self.actor(state)
action_prob = tfp.distributions.Normal(mu, sigma)
action = action_prob.sample(1)[0]
return np.clip(action, -1, 1)
def update(self, states, actions, rewards, log_probs, advantage):
states = tf.convert_to_tensor(states, dtype=tf.float32)
actions = tf.convert_to_tensor(actions, dtype=tf.float32)
rewards = tf.convert_to_tensor(rewards, dtype=tf.float32)
log_probs = tf.convert_to_tensor(log_probs, dtype=tf.float32)
advantage = tf.convert_to_tensor(advantage, dtype=tf.float32)
with tf.GradientTape() as tape:
mu, sigma = self.actor(states)
new_action_prob = tfp.distributions.Normal(mu, sigma)
new_log_probs = new_action_prob.log_prob(actions)
ratio = tf.exp(new_log_probs - log_probs)
surrogate1 = ratio * advantage
surrogate2 = tf.clip_by_value(ratio, 1.0 - 0.2, 1.0 + 0.2) * advantage
actor_loss = -tf.reduce_mean(tf.minimum(surrogate1, surrogate2))
target_values = rewards + 0.99 * self.critic(states)
critic_loss = tf.reduce_mean(tf.square(target_values - self.critic(states)))
total_loss = actor_loss + 0.5 * critic_loss
actor_gradients = tape.gradient(total_loss, self.actor.trainable_variables)
self.optimizer.apply_gradients(zip(actor_gradients, self.actor.trainable_variables))
critic_gradients = tape.gradient(critic_loss, self.critic.trainable_variables)
self.optimizer.apply_gradients(zip(critic_gradients, self.critic.trainable_variables))
# 创建环境和Agent
env = gym.make('Pendulum-v0')
agent = PPOAgent(num_actions=env.action_space.shape[0])
# 进行PPO算法的训练
for episode in range(1000):
state = env.reset()
episode_reward = 0
states, actions, rewards, log_probs, = [], [], [], []
done = False
while not done:
action = agent.get_action(state)
next_state, reward, done, _ = env.step(action)
log_prob = tfp.distributions.Normal(agent.actor(state)[0], agent.actor(state)[1]).log_prob(action)
states.append(state)
actions.append(action)
rewards.append(reward)
log_probs.append(log_prob)
state = next_state
episode_reward += reward
advantage = np.zeros_like(rewards)
target_value = 0
for i in reversed(range(len(rewards))):
delta = rewards[i] + 0.99 * target_value - agent.critic(np.array([states[i]], dtype=np.float32))
target_value = agent.critic(np.array([states[i]], dtype=np.float32))
advantage[i] = delta + 0.99 * 0.95 * advantage[i + 1]
advantage = (advantage - np.mean(advantage)) / np.std(advantage)
agent.update(states, actions, rewards, log_probs, advantage)
print('Episode: {}, Reward: {:.2f}'.format(episode+1, episode_reward))
```
这段代码使用了TensorFlow和gym库,首先定义了一个Actor模型和一个Critic模型,分别用于输出动作的均值和标准差以及评估状态值函数。然后,定义了PPOAgent类作为PPO算法的实现。在训练过程中,通过调用get_action方法获取动作,并使用update方法更新模型参数。最后,通过循环执行训练过程,并输出每个episode的奖励。
请注意,这只是一个示例代码,具体问题的实施需要根据任务和环境进行适当的调整和修改。
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