请写一段基于TensorFlow2.0的PPO2算法的代码演示

import tensorflow as tf
import tensorflow_probability as tfp# Hyperparameters
gamma = 0.95
learning_rate = 0.001# Define the PPO2 agent
class PPO2Agent:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.epsilon = 0.2
self.beta = 0.001
self.actor_model = self._build_actor_model()
self.critic_model = self._build_critic_model()
self.optimizer = tf.keras.optimizers.Adam(learning_rate)

# Build actor model
def _build_actor_model(self):
state_input = tf.keras.Input(shape=(self.state_size,))
advantage = tf.keras.Input(shape=(1,))
old_prediction = tf.keras.Input(shape=(self.action_size,))

x = tf.keras.layers.Dense(32, activation="relu")(state_input)
x = tf.keras.layers.Dense(64, activation="relu")(x)
x = tf.keras.layers.Dense(64, activation="relu")(x)
x = tf.keras.layers.Dense(self.action_size, activation="softmax")(x)

# Define the loss function
def loss(y_true, y_pred):
ratio = y_pred / (old_prediction + 1e-10)
clip_ratio = tf.clip_by_value(ratio, 1-self.epsilon, 1+self.epsilon)
surrogate_loss = tf.minimum(ratio*advantage, clip_ratio*advantage)
entropy_loss = -tf.reduce_sum(y_pred * tf.math.log(y_pred + 1e-10))
return -tf.reduce_mean(surrogate_loss - self.beta * entropy_loss) actor_model = tf.keras.Model(inputs=[state_input, advantage, old_prediction], outputs=[x])
actor_model.compile(optimizer=self.optimizer, loss=loss)
return actor_model

# Build critic model
def _build_critic_model(self):
state_input = tf.keras.Input(shape=(self.state_size,))
x = tf.keras.layers.Dense(32, activation="relu")(state_input)
x = tf.keras.layers.Dense(64, activation="relu")(x)
x = tf.keras.layers.Dense(64, activation="relu")(x)
x = tf.keras.layers.Dense(1, activation="linear")(x)
critic_model = tf.keras.Model(inputs=[state_input], outputs=[x])
critic_model.compile(optimizer=self.optimizer, loss="mse")
return critic_model

# Predict action
def predict_action(self, state):
state = np.reshape(state, [1, self.state_size])
action_probabilities = self.actor_model.predict([state, 0, 0])
action = np.random.choice(range(self.action_size), p=action_probabilities[0])
return action, action_probabilities[0]

# Train models
def train_models(self, states, actions, advantages):
old_prediction = self.actor_model.predict([states, 0, 0])
with tf.GradientTape() as tape:
loss = self.actor_model.train_on_batch([states, advantages, old_prediction], [actions])
actor_grads = tape.gradient(loss, self.actor_model.trainable_variables)
self.optimizer.apply_gradients(zip(actor_grads, self.actor_model.trainable_variables))

values = self.critic_model.predict(states)
with tf.GradientTape() as tape:
loss = self.critic_model.train_on_batch([states], [advantages + gamma * values])
critic_grads = tape.gradient(loss, self.critic_model.trainable_variables)
self.optimizer.apply_gradients(zip(critic_grads, self.critic_model.trainable_variables))

#定义PPO2代理，构建actor和critic模型，计算action的概率，训练actor和critic模型。