强化学习 14 —— TD3 算法详解与 TensorFlow 2.0 实现

一、引言

强化学习（Reinforcement Learning, RL）作为机器学习的重要分支，近年来在机器人控制、游戏AI、自动驾驶等领域取得了显著进展。其中，深度确定性策略梯度（Deep Deterministic Policy Gradient, DDPG）算法因其能够处理连续动作空间的问题而备受关注。然而，DDPG算法在实际应用中存在过估计（Overestimation）问题，导致策略性能下降。为了解决这一问题，TD3（Twin Delayed Deep Deterministic Policy Gradient）算法应运而生，它通过引入双批评家网络（Twin Critic Networks）、延迟策略更新（Delayed Policy Update）和目标策略平滑正则化（Target Policy Smoothing Regularization）等技术，有效缓解了过估计问题，提升了算法的稳定性和性能。

本文将详细解析TD3算法的核心思想、算法流程，并给出基于TensorFlow 2.0的完整实现代码，帮助读者深入理解TD3算法的原理与实践。

二、TD3算法核心思想

1. 双批评家网络

DDPG算法中，单个批评家网络（Critic Network）用于估计状态-动作对的Q值。然而，由于神经网络本身的近似误差和训练过程中的噪声，单个批评家网络容易产生过估计问题，即估计的Q值高于真实值。TD3算法通过引入双批评家网络，即两个独立的批评家网络分别估计Q值，并取两者中的较小值作为目标Q值，从而有效缓解了过估计问题。

2. 延迟策略更新

在DDPG算法中，策略网络（Actor Network）和批评家网络同时更新。然而，这种同步更新方式可能导致策略网络基于过估计的Q值进行更新，进而影响策略的性能。TD3算法采用延迟策略更新的方式，即先更新批评家网络，待批评家网络稳定后再更新策略网络，从而减少了过估计对策略更新的影响。

3. 目标策略平滑正则化

为了进一步减少过估计，TD3算法在目标Q值的计算中引入了目标策略平滑正则化。具体来说，对目标动作添加一定的噪声，然后计算多个噪声动作对应的Q值，并取平均值作为目标Q值。这种方法使得目标Q值对动作噪声更加鲁棒，从而减少了过估计。

三、TD3算法流程

TD3算法的流程如下：

1. 初始化：初始化策略网络、两个批评家网络及其目标网络，初始化经验回放缓冲区。
2. 交互：智能体与环境交互，收集状态、动作、奖励、下一状态等信息，并存入经验回放缓冲区。
3. 采样：从经验回放缓冲区中随机采样一批数据。
4. 计算目标Q值：
   • 对目标动作添加噪声，得到噪声动作。
   • 使用两个目标批评家网络分别计算噪声动作对应的Q值。
   • 取两个Q值中的较小值作为目标Q值。
5. 更新批评家网络：使用均方误差损失函数更新两个批评家网络。
6. 延迟更新策略网络：每隔一定步数，使用策略梯度更新策略网络。
7. 更新目标网络：使用软更新（Soft Update）方式更新目标批评家网络和目标策略网络。

四、TensorFlow 2.0实现

以下是基于TensorFlow 2.0的TD3算法实现代码：

import tensorflow as tf
import numpy as np
import gym
from collections import deque
import random
# 环境设置
env = gym.make('Pendulum-v0')
state_size = env.observation_space.shape[0]
action_size = env.action_space.shape[0]
action_bound = env.action_space.high[0]
# 超参数设置
BUFFER_SIZE = 10000
BATCH_SIZE = 64
GAMMA = 0.99
TAU = 0.005
LR_ACTOR = 0.001
LR_CRITIC = 0.002
EXPLORATION_NOISE = 0.1
POLICY_NOISE = 0.2
NOISE_CLIP = 0.5
POLICY_UPDATE_FREQUENCY = 2
# 经验回放缓冲区
class ReplayBuffer:
    def __init__(self, buffer_size):
        self.buffer = deque(maxlen=buffer_size)
    def add(self, state, action, reward, next_state, done):
        self.buffer.append((state, action, reward, next_state, done))
    def sample(self, batch_size):
        batch = random.sample(self.buffer, batch_size)
        state, action, reward, next_state, done = map(np.array, zip(*batch))
        return state, action, reward, next_state, done
    def size(self):
        return len(self.buffer)
# 策略网络
class Actor(tf.keras.Model):
    def __init__(self, state_size, action_size, action_bound):
        super(Actor, self).__init__()
        self.dense1 = tf.keras.layers.Dense(256, activation='relu')
        self.dense2 = tf.keras.layers.Dense(256, activation='relu')
        self.dense3 = tf.keras.layers.Dense(action_size, activation='tanh')
        self.action_bound = action_bound
    def call(self, state):
        x = self.dense1(state)
        x = self.dense2(x)
        x = self.dense3(x) * self.action_bound
        return x
# 批评家网络
class Critic(tf.keras.Model):
    def __init__(self, state_size, action_size):
        super(Critic, self).__init__()
        self.dense1 = tf.keras.layers.Dense(256, activation='relu')
        self.dense2 = tf.keras.layers.Dense(256, activation='relu')
        self.dense3 = tf.keras.layers.Dense(1)
    def call(self, state, action):
        x = tf.concat([state, action], axis=-1)
        x = self.dense1(x)
        x = self.dense2(x)
        x = self.dense3(x)
        return x
# TD3算法类
class TD3:
    def __init__(self, state_size, action_size, action_bound):
        self.state_size = state_size
        self.action_size = action_size
        self.action_bound = action_bound
        self.actor = Actor(state_size, action_size, action_bound)
        self.actor_target = Actor(state_size, action_size, action_bound)
        self.actor_target.set_weights(self.actor.get_weights())
        self.critic1 = Critic(state_size, action_size)
        self.critic2 = Critic(state_size, action_size)
        self.critic1_target = Critic(state_size, action_size)
        self.critic2_target = Critic(state_size, action_size)
        self.critic1_target.set_weights(self.critic1.get_weights())
        self.critic2_target.set_weights(self.critic2.get_weights())
        self.actor_optimizer = tf.keras.optimizers.Adam(LR_ACTOR)
        self.critic1_optimizer = tf.keras.optimizers.Adam(LR_CRITIC)
        self.critic2_optimizer = tf.keras.optimizers.Adam(LR_CRITIC)
        self.replay_buffer = ReplayBuffer(BUFFER_SIZE)
        self.policy_update_counter = 0
    def act(self, state, add_noise=True):
        state = tf.convert_to_tensor([state], dtype=tf.float32)
        action = self.actor(state).numpy()[0]
        if add_noise:
            action += np.random.normal(0, EXPLORATION_NOISE, size=self.action_size)
            action = np.clip(action, -self.action_bound, self.action_bound)
        return action
    def learn(self, state, action, reward, next_state, done):
        self.replay_buffer.add(state, action, reward, next_state, done)
        if self.replay_buffer.size() < BATCH_SIZE:
            return
        state, action, reward, next_state, done = self.replay_buffer.sample(BATCH_SIZE)
        state = tf.convert_to_tensor(state, dtype=tf.float32)
        action = tf.convert_to_tensor(action, dtype=tf.float32)
        reward = tf.convert_to_tensor(reward, dtype=tf.float32)
        next_state = tf.convert_to_tensor(next_state, dtype=tf.float32)
        done = tf.convert_to_tensor(done, dtype=tf.float32)
        # 计算目标Q值
        next_action = self.actor_target(next_state)
        noise = tf.random.normal(tf.shape(next_action), 0, POLICY_NOISE)
        noise = tf.clip_by_value(noise, -NOISE_CLIP, NOISE_CLIP)
        next_action = tf.clip_by_value(next_action + noise, -self.action_bound, self.action_bound)
        target_q1 = self.critic1_target(next_state, next_action)
        target_q2 = self.critic2_target(next_state, next_action)
        target_q = tf.minimum(target_q1, target_q2)
        target_q = reward + (1 - done) * GAMMA * target_q
        # 更新批评家网络
        with tf.GradientTape() as tape:
            current_q1 = self.critic1(state, action)
            current_q2 = self.critic2(state, action)
            critic1_loss = tf.reduce_mean(tf.square(target_q - current_q1))
            critic2_loss = tf.reduce_mean(tf.square(target_q - current_q2))
        critic1_grads = tape.gradient(critic1_loss, self.critic1.trainable_variables)
        critic2_grads = tape.gradient(critic2_loss, self.critic2.trainable_variables)
        self.critic1_optimizer.apply_gradients(zip(critic1_grads, self.critic1.trainable_variables))
        self.critic2_optimizer.apply_gradients(zip(critic2_grads, self.critic2.trainable_variables))
        # 延迟更新策略网络
        self.policy_update_counter += 1
        if self.policy_update_counter % POLICY_UPDATE_FREQUENCY == 0:
            with tf.GradientTape() as tape:
                new_action = self.actor(state)
                actor_loss = -self.critic1(state, new_action)  # 只使用critic1计算策略梯度
                actor_loss = tf.reduce_mean(actor_loss)
            actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
            self.actor_optimizer.apply_gradients(zip(actor_grads, self.actor.trainable_variables))
            # 软更新目标网络
            self.soft_update(self.critic1, self.critic1_target, TAU)
            self.soft_update(self.critic2, self.critic2_target, TAU)
            self.soft_update(self.actor, self.actor_target, TAU)
    def soft_update(self, model, model_target, tau):
        for var, var_target in zip(model.trainable_variables, model_target.trainable_variables):
            var_target.assign(tau * var + (1 - tau) * var_target)
# 训练过程
def train_td3(env, td3, episodes):
    total_rewards = []
    for episode in range(episodes):
        state = env.reset()
        episode_reward = 0
        while True:
            action = td3.act(state)
            next_state, reward, done, _ = env.step(action)
            td3.learn(state, action, reward, next_state, done)
            state = next_state
            episode_reward += reward
            if done:
                break
        total_rewards.append(episode_reward)
        if (episode + 1) % 10 == 0:
            print(f'Episode {episode + 1}, Average Reward: {np.mean(total_rewards[-10:])}')
    return total_rewards
# 主函数
if __name__ == '__main__':
    td3 = TD3(state_size, action_size, action_bound)
    total_rewards = train_td3(env, td3, 1000)

五、总结与展望

TD3算法通过引入双批评家网络、延迟策略更新和目标策略平滑正则化等技术，有效缓解了DDPG算法中的过估计问题，提升了算法的稳定性和性能。本文详细解析了TD3算法的核心思想、算法流程，并给出了基于TensorFlow 2.0的完整实现代码。未来，随着深度学习和强化学习技术的不断发展，TD3算法及其变种有望在更多复杂场景中发挥重要作用。