您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
86 行
4.6 KiB
86 行
4.6 KiB
import logging
|
|
|
|
import tensorflow as tf
|
|
from unitytrainers.models import LearningModel
|
|
|
|
logger = logging.getLogger("unityagents")
|
|
|
|
|
|
class PPOModel(LearningModel):
|
|
def __init__(self, brain, lr=1e-4, h_size=128, epsilon=0.2, beta=1e-3, max_step=5e6,
|
|
normalize=False, use_recurrent=False, num_layers=2, m_size=None):
|
|
"""
|
|
Takes a Unity environment and model-specific hyper-parameters and returns the
|
|
appropriate PPO agent model for the environment.
|
|
:param brain: BrainInfo used to generate specific network graph.
|
|
:param lr: Learning rate.
|
|
:param h_size: Size of hidden layers
|
|
:param epsilon: Value for policy-divergence threshold.
|
|
:param beta: Strength of entropy regularization.
|
|
:return: a sub-class of PPOAgent tailored to the environment.
|
|
:param max_step: Total number of training steps.
|
|
:param normalize: Whether to normalize vector observation input.
|
|
:param use_recurrent: Whether to use an LSTM layer in the network.
|
|
:param num_layers Number of hidden layers between encoded input and policy & value layers
|
|
:param m_size: Size of brain memory.
|
|
"""
|
|
LearningModel.__init__(self, m_size, normalize, use_recurrent, brain)
|
|
if num_layers < 1:
|
|
num_layers = 1
|
|
self.last_reward, self.new_reward, self.update_reward = self.create_reward_encoder()
|
|
if brain.vector_action_space_type == "continuous":
|
|
self.create_cc_actor_critic(h_size, num_layers)
|
|
self.entropy = tf.ones_like(tf.reshape(self.value, [-1])) * self.entropy
|
|
else:
|
|
self.create_dc_actor_critic(h_size, num_layers)
|
|
self.create_ppo_optimizer(self.probs, self.old_probs, self.value,
|
|
self.entropy, beta, epsilon, lr, max_step)
|
|
|
|
@staticmethod
|
|
def create_reward_encoder():
|
|
"""Creates TF ops to track and increment recent average cumulative reward."""
|
|
last_reward = tf.Variable(0, name="last_reward", trainable=False, dtype=tf.float32)
|
|
new_reward = tf.placeholder(shape=[], dtype=tf.float32, name='new_reward')
|
|
update_reward = tf.assign(last_reward, new_reward)
|
|
return last_reward, new_reward, update_reward
|
|
|
|
def create_ppo_optimizer(self, probs, old_probs, value, entropy, beta, epsilon, lr, max_step):
|
|
"""
|
|
Creates training-specific Tensorflow ops for PPO models.
|
|
:param probs: Current policy probabilities
|
|
:param old_probs: Past policy probabilities
|
|
:param value: Current value estimate
|
|
:param beta: Entropy regularization strength
|
|
:param entropy: Current policy entropy
|
|
:param epsilon: Value for policy-divergence threshold
|
|
:param lr: Learning rate
|
|
:param max_step: Total number of training steps.
|
|
"""
|
|
|
|
self.returns_holder = tf.placeholder(shape=[None], dtype=tf.float32, name='discounted_rewards')
|
|
self.advantage = tf.placeholder(shape=[None], dtype=tf.float32, name='advantages')
|
|
self.learning_rate = tf.train.polynomial_decay(lr, self.global_step, max_step, 1e-10, power=1.0)
|
|
|
|
self.old_value = tf.placeholder(shape=[None], dtype=tf.float32, name='old_value_estimates')
|
|
self.mask_input = tf.placeholder(shape=[None], dtype=tf.float32, name='masks')
|
|
|
|
decay_epsilon = tf.train.polynomial_decay(epsilon, self.global_step, max_step, 0.1, power=1.0)
|
|
decay_beta = tf.train.polynomial_decay(beta, self.global_step, max_step, 1e-5, power=1.0)
|
|
optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
|
|
|
|
self.mask = tf.equal(self.mask_input, 1.0)
|
|
|
|
clipped_value_estimate = self.old_value + tf.clip_by_value(tf.reduce_sum(value, axis=1) - self.old_value,
|
|
- decay_epsilon, decay_epsilon)
|
|
|
|
v_opt_a = tf.squared_difference(self.returns_holder, tf.reduce_sum(value, axis=1))
|
|
v_opt_b = tf.squared_difference(self.returns_holder, clipped_value_estimate)
|
|
self.value_loss = tf.reduce_mean(tf.boolean_mask(tf.maximum(v_opt_a, v_opt_b), self.mask))
|
|
|
|
self.r_theta = probs / (old_probs + 1e-10)
|
|
self.p_opt_a = self.r_theta * self.advantage
|
|
self.p_opt_b = tf.clip_by_value(self.r_theta, 1.0 - decay_epsilon, 1.0 + decay_epsilon) * self.advantage
|
|
self.policy_loss = -tf.reduce_mean(tf.boolean_mask(tf.minimum(self.p_opt_a, self.p_opt_b), self.mask))
|
|
self.loss = self.policy_loss + 0.5 * self.value_loss - decay_beta * tf.reduce_mean(
|
|
tf.boolean_mask(entropy, self.mask))
|
|
self.update_batch = optimizer.minimize(self.loss)
|