import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from carModel import CarEnvironment print("TensorFlow 版本:", tf.__version__) print("GPU 可用性:", tf.config.list_physical_devices('GPU')) # === 超参数 === EP_MAX = 1000 # 训练回合数 EP_LEN = 1800 # 每回合步长（与工况数据长度一致） GAMMA = 0.95 # 折扣因子 A_LR = 0.0001 # Actor学习率 C_LR = 0.0002 # Critic学习率 BATCH_SIZE = 128 # 训练批量大小 UPDATE_STEPS = 10 # 训练时，PPO的更新步数 EPSILON = 0.2 # PPO的裁剪系数 HIDDEN_UNITS = 128 # 隐藏层单元数 env = CarEnvironment() S_DIM = env.observation_space.shape[0] # 状态空间维度 A_DIM = env.action_space.shape[0] # 动作空间维度 A_BOUND = env.action_space.high # 动作边界 class PPOAgent: def __init__(self): # 配置GPU参数 config = tf.ConfigProto() config.gpu_options.allow_growth = True # 动态申请显存 config.gpu_options.visible_device_list = "0" # 使用第一个GPU self.sess = tf.Session(config=config) self._build_model() self.sess.run(tf.global_variables_initializer()) def _build_model(self): """ 建立 PPO 网络 """ # 1️⃣ **输入层** self.S = tf.placeholder(tf.float32, [None, S_DIM], 'state') self.A = tf.placeholder(tf.float32, [None, A_DIM], 'action') self.R = tf.placeholder(tf.float32, [None, 1], 'reward') self.advantage = tf.placeholder(tf.float32, [None, 1], 'advantage') # 2️⃣ **Critic（评估网络）** with tf.variable_scope('Critic'): c_hidden = tf.layers.dense(self.S, HIDDEN_UNITS, tf.nn.relu) self.v = tf.layers.dense(c_hidden, 1) self.critic_loss = tf.reduce_mean(tf.square(self.R - self.v)) self.critic_train_op = tf.train.AdamOptimizer(C_LR).minimize(self.critic_loss) # 3️⃣ **Actor（策略网络）** pi, pi_params = self._build_actor('pi', trainable=True) # 当前策略 oldpi, oldpi_params = self._build_actor('oldpi', trainable=False) # 旧策略

<think>嗯，用户想要用TensorFlow实现PPO算法，并且环境是Car环境，还包含超参数定义和代理类构建。首先，我需要确认PPO算法的关键点，比如策略梯度、重要性采样、Clip损失函数等。然后，考虑如何在TensorFlow中构建神经网络模型，特别是Actor-Critic结构。 用户提到的Car环境可能指的是Gym库中的CarRacing-v2或者类似的连续控制环境。这时候需要处理连续动作空间，通常使用高斯分布生成动作，并计算对应的对数概率。同时，超参数部分需要包括学习率、GAE参数、Clip范围等，这些都是PPO实现中必不可少的。 接下来，构建PPOAgent类，需要包含模型定义、动作选择、训练循环等部分。要注意的是，TensorFlow的模型构建方式，比如使用Functional API或者Subclassing。在训练过程中，需要收集多个episode的数据，计算优势估计，然后进行多次epoch的更新，每次使用mini-batch来优化损失函数。 还有，用户可能需要代码示例的结构清晰，所以需要分步骤实现环境初始化、超参数设置、代理类的方法（如act、train）。同时，要确保代码的语法正确，特别是TensorFlow 2.x的eager execution模式，避免使用过时的Session方式。 可能遇到的问题包括：如何处理连续动作空间，正确计算策略损失中的重要性比率，以及优势估计的归一化。此外，超参数的选择对训练效果影响很大，可能需要用户自行调整，比如调整clip_epsilon、学习率等。 最后，生成相关问题时要围绕PPO的实现细节、超参数调优、环境适配以及性能优化等方面，帮助用户深入理解相关主题。</think>以下是一个使用TensorFlow实现PPO算法与CarRacing-v2环境的完整实现，包含超参数定义和代理类构建： ```python import tensorflow as tf from tensorflow.keras import layers import gym import numpy as np # 环境初始化 env = gym.make('CarRacing-v2') state_dim = env.observation_space.shape # (96, 96, 3) action_dim = env.action_space.shape[0] # 连续动作空间（转向，油门，刹车） # 超参数配置 class PPOConfig: GAMMA = 0.99 # 折扣因子 LAMBDA = 0.95 # GAE参数 CLIP_EPSILON = 0.2 # 策略更新裁剪范围 EPOCHS = 10 # 优化迭代次数 BATCH_SIZE = 64 # 小批量大小 LR_ACTOR = 3e-4 # 策略网络学习率 LR_CRITIC = 1e-3 # 价值网络学习率 ENTROPY_COEF = 0.01 # 熵正则化系数 VALUE_COEF = 0.5 # 价值损失系数 # Actor-Critic网络架构 class ActorCritic(tf.keras.Model): def __init__(self): super().__init__() self.conv1 = layers.Conv2D(32, 8, 4, activation='relu') self.conv2 = layers.Conv2D(64, 4, 2, activation='relu') self.conv3 = layers.Conv2D(64, 3, 1, activation='relu') self.flatten = layers.Flatten() self.common = layers.Dense(512, activation='relu') self.actor = layers.Dense(action_dim * 2) # 均值和标准差 self.critic = layers.Dense(1) def call(self, inputs): x = self.conv1(inputs) x = self.conv2(x) x = self.conv3(x) x = self.flatten(x) x = self.common(x) mean_std = self.actor(x) mean, log_std = tf.split(mean_std, 2, axis=-1) log_std = tf.tanh(log_std) # 约束标准差范围 return mean, log_std, self.critic(x) # PPO代理类 class PPOAgent: def __init__(self, config=PPOConfig()): self.model = ActorCritic() self.opt_actor = tf.keras.optimizers.Adam(config.LR_ACTOR) self.opt_critic = tf.keras.optimizers.Adam(config.LR_CRITIC) self.config = config def get_action(self, state): state = tf.expand_dims(tf.convert_to_tensor(state), 0) mean, log_std, _ = self.model(state) std = tf.exp(log_std) * 0.5 + 1e-5 # 确保标准差正定 dist = tfp.distributions.Normal(mean, std) action = dist.sample() log_prob = tf.reduce_sum(dist.log_prob(action), axis=-1) action = tf.tanh(action) # 将动作约束到[-1, 1]范围 return action.numpy()[0], log_prob.numpy()[0] def compute_advantages(self, rewards, values, dones): advantages = np.zeros_like(rewards) last_advantage = 0 for t in reversed(range(len(rewards))): delta = rewards[t] + self.config.GAMMA * values[t+1] * (1-dones[t]) - values[t] advantages[t] = delta + self.config.GAMMA * self.config.LAMBDA * (1-dones[t]) * last_advantage last_advantage = advantages[t] return (advantages - advantages.mean()) / (advantages.std() + 1e-8) def train(self, states, actions, old_log_probs, advantages, returns): dataset = tf.data.Dataset.from_tensor_slices((states, actions, old_log_probs, advantages, returns)) dataset = dataset.shuffle(1024).batch(self.config.BATCH_SIZE) for _ in range(self.config.EPOCHS): for batch in dataset: self.update_actor(batch) self.update_critic(batch) @tf.function def update_actor(self, batch): states, actions, old_log_probs, advantages, _ = batch with tf.GradientTape() as tape: mean, log_std, _ = self.model(states) std = tf.exp(log_std) * 0.5 + 1e-5 dist = tfp.distributions.Normal(mean, std) log_probs = tf.reduce_sum(dist.log_prob(actions), axis=-1) ratio = tf.exp(log_probs - old_log_probs) clipped_ratio = tf.clip_by_value(ratio, 1-self.config.CLIP_EPSILON, 1+self.config.CLIP_EPSILON) actor_loss = -tf.reduce_mean(tf.minimum(ratio * advantages, clipped_ratio * advantages)) entropy = tf.reduce_mean(dist.entropy()) total_loss = actor_loss - self.config.ENTROPY_COEF * entropy grads = tape.gradient(total_loss, self.model.trainable_variables) self.opt_actor.apply_gradients(zip(grads, self.model.trainable_variables)) @tf.function def update_critic(self, batch): states, _, _, _, returns = batch with tf.GradientTape() as tape: _, _, values = self.model(states) critic_loss = self.config.VALUE_COEF * tf.reduce_mean((returns - values)**2) grads = tape.gradient(critic_loss, self.model.trainable_variables) self.opt_critic.apply_gradients(zip(grads, self.model.trainable_variables)) ```