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import tensorflow as tf
import keras
from keras import layers, Model
import numpy as np
import tensorflow_probability as tfp
import os
import traceback
tfd = tfp.distributions
@tf.keras.utils.register_keras_serializable()
class Actor(Model):
def __init__(self, obs_shape, action_size, hidden_layer_sizes=[512, 512, 512], **kwargs):
super().__init__(**kwargs)
if len(obs_shape) > 1:
self.flatten = layers.Flatten(input_shape=obs_shape)
self.flatten(tf.zeros((1,) + obs_shape))
else:
self.flatten = None
self.dense_layers = []
for size in hidden_layer_sizes:
self.dense_layers.append(layers.Dense(size, activation='relu'))
self.logits = layers.Dense(action_size)
self._obs_shape = obs_shape
self._action_size = action_size
self._hidden_layer_sizes = hidden_layer_sizes
def call(self, inputs):
x = self.flatten(inputs) if self.flatten else inputs
for layer in self.dense_layers:
x = layer(x)
return self.logits(x)
def get_config(self):
config = super().get_config()
config.update({
'obs_shape': self._obs_shape,
'action_size': self._action_size,
'hidden_layer_sizes': self._hidden_layer_sizes
})
return config
@tf.keras.utils.register_keras_serializable()
class Critic(Model):
def __init__(self, obs_shape, hidden_layer_sizes=[512, 512, 512], **kwargs):
super().__init__(**kwargs)
if len(obs_shape) > 1:
self.flatten = layers.Flatten(input_shape=obs_shape)
self.flatten(tf.zeros((1,) + obs_shape))
else:
self.flatten = None
self.dense_layers = []
for size in hidden_layer_sizes:
self.dense_layers.append(layers.Dense(size, activation='relu'))
self.value = layers.Dense(1)
self._obs_shape = obs_shape
self._hidden_layer_sizes = hidden_layer_sizes
def call(self, inputs):
x = self.flatten(inputs) if self.flatten else inputs
for layer in self.dense_layers:
x = layer(x)
return self.value(x)
def get_config(self):
config = super().get_config()
config.update({
'obs_shape': self._obs_shape,
'hidden_layer_sizes': self._hidden_layer_sizes
})
return config
class PPOAgent:
def __init__(self, observation_space_shape, action_space_size,
actor_lr=3e-4, critic_lr=3e-4, gamma=0.99,
gae_lambda=0.95, clip_epsilon=0.2,
num_epochs_per_update=10, batch_size=64,
hidden_layer_sizes=[512, 512, 512]):
self.gamma = gamma
self.gae_lambda = gae_lambda
self.clip_epsilon = clip_epsilon
self.num_epochs_per_update = num_epochs_per_update
self.batch_size = batch_size
self.observation_space_shape = observation_space_shape
self.action_space_size = action_space_size
self.actor = Actor(observation_space_shape, action_space_size, hidden_layer_sizes=hidden_layer_sizes)
self.critic = Critic(observation_space_shape, hidden_layer_sizes=hidden_layer_sizes)
self.actor_optimizer = tf.keras.optimizers.Adam(learning_rate=actor_lr)
self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=critic_lr)
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.dones = []
self.log_probs = []
self.values = []
dummy_obs = tf.zeros((1,) + observation_space_shape, dtype=tf.float32)
self.actor(dummy_obs)
self.critic(dummy_obs)
def remember(self, state, action, reward, next_state, done, log_prob, value):
self.states.append(state)
self.actions.append(action)
self.rewards.append(reward)
self.next_states.append(next_state)
self.dones.append(done)
self.log_probs.append(log_prob)
self.values.append(value)
@tf.function
def _choose_action_tf(self, observation, action_mask):
observation = tf.expand_dims(tf.convert_to_tensor(observation, dtype=tf.float32), 0)
pi_logits = self.actor(observation)
masked_logits = tf.where(action_mask, pi_logits, -1e9)
value = self.critic(observation)
distribution = tfd.Categorical(logits=masked_logits)
action = distribution.sample()
log_prob = distribution.log_prob(action)
return action, log_prob, value
def choose_action(self, observation, action_mask):
action_tensor, log_prob_tensor, value_tensor = self._choose_action_tf(observation, action_mask)
return action_tensor.numpy(), log_prob_tensor.numpy(), value_tensor.numpy()[0,0]
def calculate_advantages_and_returns(self):
rewards = np.array(self.rewards, dtype=np.float32)
values = np.array(self.values, dtype=np.float32)
dones = np.array(self.dones, dtype=np.float32)
last_next_state_value = self.critic(tf.expand_dims(tf.convert_to_tensor(self.next_states[-1], dtype=tf.float32), 0)).numpy()[0,0] if not dones[-1] else 0
next_values = np.append(values[1:], last_next_state_value)
advantages = []
returns = []
last_advantage = 0
for t in reversed(range(len(rewards))):
delta = rewards[t] + self.gamma * next_values[t] * (1 - dones[t]) - values[t]
advantage = delta + self.gae_lambda * self.gamma * (1 - dones[t]) * last_advantage
advantages.insert(0, advantage)
returns.insert(0, advantage + values[t])
last_advantage = advantage
return np.array(advantages, dtype=np.float32), np.array(returns, dtype=np.float32)
def learn(self):
if not self.states:
return
states = tf.convert_to_tensor(np.array(self.states), dtype=tf.float32)
actions = tf.convert_to_tensor(np.array(self.actions), dtype=tf.int32)
old_log_probs = tf.convert_to_tensor(np.array(self.log_probs), dtype=tf.float32)
advantages, returns = self.calculate_advantages_and_returns()
advantages = (advantages - tf.reduce_mean(advantages)) / (tf.math.reduce_std(advantages) + 1e-8)
dataset = tf.data.Dataset.from_tensor_slices((states, actions, old_log_probs, advantages, returns))
dataset = dataset.shuffle(buffer_size=len(self.states)).batch(self.batch_size)
for _ in range(self.num_epochs_per_update):
for batch_states, batch_actions, batch_old_log_probs, batch_advantages, batch_returns in dataset:
with tf.GradientTape() as tape:
current_logits = self.actor(batch_states)
new_distribution = tfd.Categorical(logits=current_logits)
new_log_probs = new_distribution.log_prob(batch_actions)
ratio = tf.exp(new_log_probs - batch_old_log_probs)
surrogate1 = ratio * batch_advantages
surrogate2 = tf.clip_by_value(ratio, 1 - self.clip_epsilon, 1 + self.clip_epsilon) * batch_advantages
actor_loss = -tf.reduce_mean(tf.minimum(surrogate1, surrogate2))
actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
self.actor_optimizer.apply_gradients(zip(actor_grads, self.actor.trainable_variables))
with tf.GradientTape() as tape:
new_values = self.critic(batch_states)
critic_loss = tf.reduce_mean(tf.square(new_values - batch_returns))
critic_grads = tape.gradient(critic_loss, self.critic.trainable_variables)
self.critic_optimizer.apply_gradients(zip(critic_grads, self.critic.trainable_variables))
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.dones = []
self.log_probs = []
self.values = []
def save_models(self, path):
actor_save_path = f"{path}_actor.keras"
critic_save_path = f"{path}_critic.keras"
print(f"\n--- Attempting to save models ---")
print(f"Target Actor path: {os.path.abspath(actor_save_path)}")
print(f"Target Critic path: {os.path.abspath(critic_save_path)}")
try:
self.actor.save(actor_save_path)
print(f"Actor model saved successfully to {os.path.abspath(actor_save_path)}")
except Exception as e:
print(f"ERROR: Failed to save Actor model to {os.path.abspath(actor_save_path)}")
print(f"Reason: {e}")
traceback.print_exc()
try:
self.critic.save(critic_save_path)
print(f"Critic model saved successfully to {os.path.abspath(critic_save_path)}")
except Exception as e:
print(f"ERROR: Failed to save Critic model to {os.path.abspath(critic_save_path)}")
print(f"Reason: {e}")
traceback.print_exc()
print(f"--- Models save process completed ---\n")
def load_models(self, path):
actor_load_path = f"{path}_actor.keras"
critic_load_path = f"{path}_critic.keras"
actor_loaded_ok = False
critic_loaded_ok = False
custom_objects = {
'Actor': Actor,
'Critic': Critic
}
try:
self.actor = tf.keras.models.load_model(actor_load_path, custom_objects=custom_objects)
actor_loaded_ok = True
print(f"Actor model loaded from: {os.path.abspath(actor_load_path)}")
except Exception as e:
print(f"ERROR: Failed to load Actor model from {os.path.abspath(actor_load_path)}")
print(f"Reason: {e}")
traceback.print_exc()
try:
self.critic = tf.keras.models.load_model(critic_load_path, custom_objects=custom_objects)
critic_loaded_ok = True
print(f"Critic model loaded from: {os.path.abspath(critic_load_path)}")
except Exception as e:
print(f"ERROR: Failed to load Critic model from {os.path.abspath(critic_load_path)}")
print(f"Reason: {e}")
traceback.print_exc()
if actor_loaded_ok and critic_loaded_ok:
print(f"All PPO models loaded successfully from '{path}'.")
return True
else:
print(f"Warning: One or both models failed to load. The agent will use untrained models.")
return False
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