使用copilot写一个模仿学习

'''
 写一个简单的模仿学习  
'''
import gym  
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import optimizers
from tensorflow.keras import losses
from tensorflow.keras import metrics
from tensorflow.keras import backend as K
import matplotlib.pyplot as plt
import random
import os
import time


# 1. 环境
env = gym.make('CartPole-v0')
env.seed(1)
env = env.unwrapped

# 2. 神经网络
class Actor(keras.Model):
    def __init__(self, state_dim, action_dim):
        super(Actor, self).__init__()
        self.fc1 = layers.Dense(20, activation='relu')
        self.fc2 = layers.Dense(20, activation='relu')
        self.out = layers.Dense(action_dim, activation='softmax')

    def call(self, inputs):
        x = self.fc1(inputs)
        x = self.fc2(x)
        x = self.out(x)
        return x

# 3. 模型
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
actor = Actor(state_dim, action_dim)
actor.build(input_shape=(None, state_dim))

# 4. 模型训练
def train():
    # 4.1. 专家数据
    expert_data = []
    for i_episode in range(20):
        observation = env.reset()
        while True:
            action = env.action_space.sample()
            observation_, reward, done, info = env.step(action)
            expert_data.append((observation, action))
            observation = observation_
            if done:
                break

    # 4.2. 模型训练
    actor.compile(optimizer=optimizers.Adam(0.001), loss=losses.sparse_categorical_crossentropy)
    actor.fit(np.array([x[0] for x in expert_data]), np.array([x[1] for x in expert_data]), epochs=10, verbose=0)

    # 4.3. 模型评估
    for i_episode in range(5):
        observation = env.reset()
        while True:
            env.render()
            action = np.argmax(actor.predict(observation[np.newaxis, :]))
            observation_, reward, done, info = env.step(action)
            observation = observation_
            if done:
                break

if __name__ == '__main__':
    train()