逆向强化学习实例代码

以下是一个简单的逆向强化学习实例代码，使用了Python和TensorFlow库：

import numpy as np
import tensorflow as tf

# 定义环境模型，这里使用一个简单的网格世界
class GridWorld:
    def __init__(self):
        self.grid = np.zeros((4, 4))
        self.grid[3, 3] = 1  # 目标位置
        self.state = (0, 0)  # 初始状态

    def step(self, action):
        if action == 'up':
            self.state = (max(self.state[0] - 1, 0), self.state[1])
        elif action == 'down':
            self.state = (min(self.state[0] + 1, 3), self.state[1])
        elif action == 'left':
            self.state = (self.state[0], max(self.state[1] - 1, 0))
        elif action == 'right':
            self.state = (self.state[0], min(self.state[1] + 1, 3))

        if self.state == (3, 3):
            reward = 1
            done = True
        else:
            reward = 0
            done = False

        return self.state, reward, done

# 定义逆向强化学习算法
class IRL:
    def __init__(self, expert_trajectories, num_states, num_actions, learning_rate=0.01):
        self.expert_trajectories = expert_trajectories
        self.num_states = num_states
        self.num_actions = num_actions
        self.learning_rate = learning_rate

        self.weights = tf.Variable(tf.random.normal((num_states, num_actions)))

    def compute_policy(self, state):
        logits = tf.matmul(state, self.weights)
        return tf.nn.softmax(logits)

    def train(self):
        optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)

        for expert_trajectory in self.expert_trajectories:
            states, actions = expert_trajectory

            with tf.GradientTape() as tape:
                state_tensor = tf.convert_to_tensor(states, dtype=tf.float32)
                action_tensor = tf.one_hot(actions, depth=self.num_actions)

                policy = self.compute_policy(state_tensor)
                loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=action_tensor, logits=policy))

            gradients = tape.gradient(loss, [self.weights])
            optimizer.apply_gradients(zip(gradients, [self.weights]))

# 创建一个网格世界环境实例
env = GridWorld()

# 生成一些专家轨迹数据
expert_trajectories = [([env.step('right')[0] for _ in range(4)], ['right', 'right', 'right', 'down']),
                       ([env.step('down')[0] for _ in range(4)], ['down', 'down', 'down', 'right'])]

# 创建一个逆向强化学习模型实例
irl = IRL(expert_trajectories, num_states=16, num_actions=4)

# 训练逆向强化学习模型
irl.train()

这个例子展示了一个简单的网格世界环境，使用逆向强化学习算法从专家轨迹中学习一个策略。模型通过迭代优化权重来逼近专家的行为特征。请注意，这只是一个简单的示例，实际中可能需要更复杂的环境和更强大的模型来解决实际问题。