ml-agents/ml-agents/mlagents/trainers/ppo/models.py

import logging
import numpy as np

import tensorflow as tf
from mlagents.trainers.models import LearningModel

logger = logging.getLogger("mlagents.trainers")


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,
        seed=0,
        stream_names=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.
        :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.
        :param seed: Seed to use for initialization of model. 
        :param stream_names: List of names of value streams. Usually, a list of the Reward Signals being used. 
        :return: a sub-class of PPOAgent tailored to the environment.
        """
        LearningModel.__init__(
            self, m_size, normalize, use_recurrent, brain, seed, stream_names
        )
        if num_layers < 1:
            num_layers = 1
        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_losses(
            self.log_probs,
            self.old_log_probs,
            self.value_heads,
            self.entropy,
            beta,
            epsilon,
            lr,
            max_step,
        )

    def create_losses(
        self, probs, old_probs, value_heads, 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_heads: Value estimate tensors from each value stream
        :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_holders = {}
        self.old_values = {}
        for name in value_heads.keys():
            returns_holder = tf.placeholder(
                shape=[None], dtype=tf.float32, name="{}_returns".format(name)
            )
            old_value = tf.placeholder(
                shape=[None], dtype=tf.float32, name="{}_value_estimate".format(name)
            )
            self.returns_holders[name] = returns_holder
            self.old_values[name] = old_value
        self.advantage = tf.placeholder(
            shape=[None, 1], dtype=tf.float32, name="advantages"
        )
        self.learning_rate = tf.train.polynomial_decay(
            lr, self.global_step, max_step, 1e-10, power=1.0
        )

        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
        )

        value_losses = []
        for name, head in value_heads.items():
            clipped_value_estimate = self.old_values[name] + tf.clip_by_value(
                tf.reduce_sum(head, axis=1) - self.old_values[name],
                -decay_epsilon,
                decay_epsilon,
            )
            v_opt_a = tf.squared_difference(
                self.returns_holders[name], tf.reduce_sum(head, axis=1)
            )
            v_opt_b = tf.squared_difference(
                self.returns_holders[name], clipped_value_estimate
            )
            value_loss = tf.reduce_mean(
                tf.dynamic_partition(tf.maximum(v_opt_a, v_opt_b), self.mask, 2)[1]
            )
            value_losses.append(value_loss)
        self.value_loss = tf.reduce_mean(value_losses)

        r_theta = tf.exp(probs - old_probs)
        p_opt_a = r_theta * self.advantage
        p_opt_b = (
            tf.clip_by_value(r_theta, 1.0 - decay_epsilon, 1.0 + decay_epsilon)
            * self.advantage
        )
        self.policy_loss = -tf.reduce_mean(
            tf.dynamic_partition(tf.minimum(p_opt_a, p_opt_b), self.mask, 2)[1]
        )

        self.loss = (
            self.policy_loss
            + 0.5 * self.value_loss
            - decay_beta
            * tf.reduce_mean(tf.dynamic_partition(entropy, self.mask, 2)[1])
        )

    def create_ppo_optimizer(self):
        optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
        self.update_batch = optimizer.minimize(self.loss)