模仿学习A2C python

以下是使用 Python 实现模仿学习和 A2C 算法训练智能体的代码示例：

（1）安装相关依赖库

pip install tensorflow gym

（2）定义智能体的神经网络结构

import tensorflow as tf

class ActorCritic(tf.keras.Model):
    def __init__(self, action_space):
        super(ActorCritic, self).__init__()
        self.dense1 = tf.keras.layers.Dense(128, activation='relu')
        self.policy_logits = tf.keras.layers.Dense(action_space)
        self.dense2 = tf.keras.layers.Dense(128, activation='relu')
        self.values = tf.keras.layers.Dense(1)

    def call(self, inputs):
        x = self.dense1(inputs)
        logits = self.policy_logits(x)
        v = self.dense2(inputs)
        values = self.values(v)
        return logits, values

（3）定义损失函数和优化器

import numpy as np

def compute_loss(logits, values, actions, rewards, dones):
    advantage = rewards - values
    value_loss = advantage**2

    policy = tf.nn.softmax(logits)
    entropy = tf.reduce_sum(policy * tf.math.log(policy + 1e-20), axis=1)

    log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=actions)
    policy_loss = log_prob * tf.stop_gradient(advantage)
    policy_loss -= 0.01 * entropy

    mask = tf.cast(dones, dtype=tf.float32)
    value_loss *= mask
    policy_loss *= mask

    return tf.reduce_mean(value_loss + policy_loss)

optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)

（4）定义环境和动作空间

import gym

env = gym.make('CartPole-v0')
action_space = env.action_space.n

（5）定义模仿学习的数据集

import random

def get_expert_data(env, n_episodes=10):
    expert_data = []
    for _ in range(n_episodes):
        obs = env.reset()
        done = False
        while not done:
            action = env.action_space.sample()
            expert_data.append((obs, action))
            obs, reward, done, _ = env.step(action)
    return expert_data

expert_data = get_expert_data(env)
random.shuffle(expert_data)

（6）使用模仿学习的数据集来训练智能体的初始策略

for obs, action in expert_data:
    with tf.GradientTape() as tape:
        logits, values = model(tf.convert_to_tensor(obs[None, :], dtype=tf.float32))
        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.convert_to_tensor([action]))
    grads = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))

（7）使用 A2C 算法对智能体进行训练

def train(env, model, optimizer, n_episodes=1000):
    for episode in range(n_episodes):
        obs = env.reset()
        done = False
        episode_reward = 0

        while not done:
            with tf.GradientTape() as tape:
                logits, values = model(tf.convert_to_tensor(obs[None, :], dtype=tf.float32))
                action = tf.random.categorical(logits, 1)[0, 0]
                next_obs, reward, done, _ = env.step(action.numpy())

                episode_reward += reward

                next_logits, next_values = model(tf.convert_to_tensor(next_obs[None, :], dtype=tf.float32))
                td_target = reward + 0.99 * next_values[0, 0] * (1 - int(done))

                advantage = td_target - values[0, 0]
                value_loss = advantage**2

                log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.convert_to_tensor([action]))
                policy_loss = log_prob * tf.stop_gradient(advantage)
                entropy = tf.reduce_sum(tf.nn.softmax(logits) * tf.math.log(tf.nn.softmax(logits) + 1e-20))

                loss = value_loss + policy_loss - 0.01 * entropy

            grads = tape.gradient(loss, model.trainable_variables)
            optimizer.apply_gradients(zip(grads, model.trainable_variables))

            obs = next_obs

        print("Episode: {}, Reward: {}".format(episode, episode_reward))

（8）对训练好的智能体进行评估和测试

def evaluate(env, model):
    obs = env.reset()
    done = False
    total_reward = 0
    while not done:
        logits, _ = model(tf.convert_to_tensor(obs[None, :], dtype=tf.float32))
        action = tf.argmax(logits, axis=-1)
        obs, reward, done, _ = env.step(action.numpy())
        total_reward += reward
    return total_reward

train(env, model, optimizer)
print("Test Reward: {}".format(evaluate(env, model)))

需要注意的是，以上代码示例仅供参考，实际应用中可能需要根据具体情况进行调整和修改，以获得更好的效果。