actor-critic算法python代码

我可以提供一组简单的actor-critic算法的python代码示例:

import numpy as np

class ActorCritic:
    def __init__(self, env, alpha, beta, gamma):
        self.env = env
        self.alpha = alpha
        self.beta = beta
        self.gamma = gamma
        self.state = env.reset()
        self.theta = np.random.rand(self.state.shape[0], env.action_space.n)
        self.w = np.random.rand(self.state.shape[0], 1)

    def predict(self, state):
        return self.theta @ state

    def update(self, state, action, reward, next_state):
        # update actor
        delta = reward + self.gamma * (self.w.T @ next_state) - (self.w.T @ state)
        self.theta[:, action] += self.alpha * delta * state

        # update critic
        delta = reward + self.gamma * (self.w.T @ next_state) - (self.w.T @ state)
        self.w += self.beta * delta * state

    def act(self):
        action = np.argmax(self.predict(self.state))
        next_state, reward, done, _ = self.env.step(action)
        self.update(self.state, action, reward, next_state)
        self.state = next_state
        return done

请注意，这只是一个简单示例，在实际使用中还需要进行其他调整和优化。