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362 行
14 KiB
362 行
14 KiB
from typing import Optional, Any, Dict, cast
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import numpy as np
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from mlagents.tf_utils import tf
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from mlagents_envs.timers import timed
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from mlagents.trainers.tf.models import ModelUtils, EncoderType
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from mlagents.trainers.policy.tf_policy import TFPolicy
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from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer
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from mlagents.trainers.buffer import AgentBuffer
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from mlagents.trainers.settings import TrainerSettings, PPOSettings
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class PPOOptimizer(TFOptimizer):
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def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings):
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"""
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Takes a Policy and a Dict of trainer parameters and creates an Optimizer around the policy.
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The PPO optimizer has a value estimator and a loss function.
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:param policy: A TFPolicy object that will be updated by this PPO Optimizer.
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:param trainer_params: Trainer parameters dictionary that specifies the properties of the trainer.
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"""
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# Create the graph here to give more granular control of the TF graph to the Optimizer.
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policy.create_tf_graph()
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with policy.graph.as_default():
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with tf.variable_scope("optimizer/"):
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super().__init__(policy, trainer_params)
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hyperparameters: PPOSettings = cast(
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PPOSettings, trainer_params.hyperparameters
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)
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lr = float(hyperparameters.learning_rate)
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self._schedule = hyperparameters.learning_rate_schedule
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epsilon = float(hyperparameters.epsilon)
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beta = float(hyperparameters.beta)
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max_step = float(trainer_params.max_steps)
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policy_network_settings = policy.network_settings
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h_size = int(policy_network_settings.hidden_units)
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num_layers = policy_network_settings.num_layers
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vis_encode_type = policy_network_settings.vis_encode_type
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self.burn_in_ratio = 0.0
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self.stream_names = list(self.reward_signals.keys())
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self.tf_optimizer: Optional[tf.train.AdamOptimizer] = None
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self.grads = None
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self.update_batch: Optional[tf.Operation] = None
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self.stats_name_to_update_name = {
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"Losses/Value Loss": "value_loss",
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"Losses/Policy Loss": "policy_loss",
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"Policy/Learning Rate": "learning_rate",
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"Policy/Epsilon": "decay_epsilon",
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"Policy/Beta": "decay_beta",
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}
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if self.policy.use_recurrent:
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self.m_size = self.policy.m_size
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self.memory_in = tf.placeholder(
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shape=[None, self.m_size],
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dtype=tf.float32,
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name="recurrent_value_in",
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)
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if num_layers < 1:
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num_layers = 1
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if policy.use_continuous_act:
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self._create_cc_critic(h_size, num_layers, vis_encode_type)
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else:
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self._create_dc_critic(h_size, num_layers, vis_encode_type)
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self.learning_rate = ModelUtils.create_schedule(
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self._schedule,
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lr,
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self.policy.global_step,
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int(max_step),
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min_value=1e-10,
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)
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self._create_losses(
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self.policy.total_log_probs,
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self.old_log_probs,
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self.value_heads,
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self.policy.entropy,
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beta,
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epsilon,
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lr,
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max_step,
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)
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self._create_ppo_optimizer_ops()
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self.update_dict.update(
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{
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"value_loss": self.value_loss,
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"policy_loss": self.abs_policy_loss,
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"update_batch": self.update_batch,
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"learning_rate": self.learning_rate,
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"decay_epsilon": self.decay_epsilon,
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"decay_beta": self.decay_beta,
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}
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)
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self.policy.initialize_or_load()
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def _create_cc_critic(
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self, h_size: int, num_layers: int, vis_encode_type: EncoderType
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) -> None:
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"""
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Creates Continuous control critic (value) network.
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:param h_size: Size of hidden linear layers.
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:param num_layers: Number of hidden linear layers.
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:param vis_encode_type: The type of visual encoder to use.
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"""
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hidden_stream = ModelUtils.create_observation_streams(
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self.policy.visual_in,
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self.policy.processed_vector_in,
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1,
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h_size,
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num_layers,
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vis_encode_type,
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)[0]
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if self.policy.use_recurrent:
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hidden_value, memory_value_out = ModelUtils.create_recurrent_encoder(
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hidden_stream,
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self.memory_in,
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self.policy.sequence_length_ph,
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name="lstm_value",
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)
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self.memory_out = memory_value_out
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else:
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hidden_value = hidden_stream
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self.value_heads, self.value = ModelUtils.create_value_heads(
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self.stream_names, hidden_value
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)
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self.all_old_log_probs = tf.placeholder(
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shape=[None, sum(self.policy.act_size)],
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dtype=tf.float32,
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name="old_probabilities",
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)
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self.old_log_probs = tf.reduce_sum(
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(tf.identity(self.all_old_log_probs)), axis=1, keepdims=True
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)
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def _create_dc_critic(
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self, h_size: int, num_layers: int, vis_encode_type: EncoderType
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) -> None:
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"""
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Creates Discrete control critic (value) network.
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:param h_size: Size of hidden linear layers.
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:param num_layers: Number of hidden linear layers.
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:param vis_encode_type: The type of visual encoder to use.
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"""
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hidden_stream = ModelUtils.create_observation_streams(
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self.policy.visual_in,
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self.policy.processed_vector_in,
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1,
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h_size,
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num_layers,
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vis_encode_type,
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)[0]
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if self.policy.use_recurrent:
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hidden_value, memory_value_out = ModelUtils.create_recurrent_encoder(
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hidden_stream,
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self.memory_in,
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self.policy.sequence_length_ph,
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name="lstm_value",
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)
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self.memory_out = memory_value_out
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else:
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hidden_value = hidden_stream
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self.value_heads, self.value = ModelUtils.create_value_heads(
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self.stream_names, hidden_value
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)
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self.all_old_log_probs = tf.placeholder(
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shape=[None, sum(self.policy.act_size)],
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dtype=tf.float32,
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name="old_probabilities",
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)
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# Break old log probs into separate branches
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old_log_prob_branches = ModelUtils.break_into_branches(
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self.all_old_log_probs, self.policy.act_size
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)
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_, _, old_normalized_logits = ModelUtils.create_discrete_action_masking_layer(
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old_log_prob_branches, self.policy.action_masks, self.policy.act_size
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)
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action_idx = [0] + list(np.cumsum(self.policy.act_size))
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self.old_log_probs = tf.reduce_sum(
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(
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tf.stack(
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[
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-tf.nn.softmax_cross_entropy_with_logits_v2(
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labels=self.policy.selected_actions[
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:, action_idx[i] : action_idx[i + 1]
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],
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logits=old_normalized_logits[
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:, action_idx[i] : action_idx[i + 1]
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],
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)
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for i in range(len(self.policy.act_size))
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],
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axis=1,
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)
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),
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axis=1,
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keepdims=True,
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)
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def _create_losses(
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self, probs, old_probs, value_heads, entropy, beta, epsilon, lr, max_step
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):
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"""
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Creates training-specific Tensorflow ops for PPO models.
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:param probs: Current policy probabilities
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:param old_probs: Past policy probabilities
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:param value_heads: Value estimate tensors from each value stream
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:param beta: Entropy regularization strength
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:param entropy: Current policy entropy
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:param epsilon: Value for policy-divergence threshold
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:param lr: Learning rate
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:param max_step: Total number of training steps.
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"""
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self.returns_holders = {}
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self.old_values = {}
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for name in value_heads.keys():
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returns_holder = tf.placeholder(
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shape=[None], dtype=tf.float32, name=f"{name}_returns"
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)
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old_value = tf.placeholder(
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shape=[None], dtype=tf.float32, name=f"{name}_value_estimate"
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)
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self.returns_holders[name] = returns_holder
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self.old_values[name] = old_value
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self.advantage = tf.placeholder(
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shape=[None], dtype=tf.float32, name="advantages"
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)
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advantage = tf.expand_dims(self.advantage, -1)
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self.decay_epsilon = ModelUtils.create_schedule(
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self._schedule, epsilon, self.policy.global_step, max_step, min_value=0.1
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)
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self.decay_beta = ModelUtils.create_schedule(
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self._schedule, beta, self.policy.global_step, max_step, min_value=1e-5
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)
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value_losses = []
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for name, head in value_heads.items():
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clipped_value_estimate = self.old_values[name] + tf.clip_by_value(
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tf.reduce_sum(head, axis=1) - self.old_values[name],
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-self.decay_epsilon,
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self.decay_epsilon,
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)
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v_opt_a = tf.squared_difference(
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self.returns_holders[name], tf.reduce_sum(head, axis=1)
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)
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v_opt_b = tf.squared_difference(
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self.returns_holders[name], clipped_value_estimate
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)
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value_loss = tf.reduce_mean(
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tf.dynamic_partition(tf.maximum(v_opt_a, v_opt_b), self.policy.mask, 2)[
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1
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]
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)
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value_losses.append(value_loss)
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self.value_loss = tf.reduce_mean(value_losses)
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r_theta = tf.exp(probs - old_probs)
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p_opt_a = r_theta * advantage
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p_opt_b = (
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tf.clip_by_value(
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r_theta, 1.0 - self.decay_epsilon, 1.0 + self.decay_epsilon
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)
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* advantage
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)
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self.policy_loss = -tf.reduce_mean(
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tf.dynamic_partition(tf.minimum(p_opt_a, p_opt_b), self.policy.mask, 2)[1]
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)
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# For cleaner stats reporting
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self.abs_policy_loss = tf.abs(self.policy_loss)
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self.loss = (
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self.policy_loss
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+ 0.5 * self.value_loss
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- self.decay_beta
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* tf.reduce_mean(tf.dynamic_partition(entropy, self.policy.mask, 2)[1])
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)
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def _create_ppo_optimizer_ops(self):
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self.tf_optimizer = self.create_optimizer_op(self.learning_rate)
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self.grads = self.tf_optimizer.compute_gradients(self.loss)
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self.update_batch = self.tf_optimizer.minimize(self.loss)
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@timed
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def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
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"""
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Performs update on model.
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:param mini_batch: Batch of experiences.
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:param num_sequences: Number of sequences to process.
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:return: Results of update.
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"""
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feed_dict = self._construct_feed_dict(batch, num_sequences)
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stats_needed = self.stats_name_to_update_name
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update_stats = {}
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# Collect feed dicts for all reward signals.
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for _, reward_signal in self.reward_signals.items():
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feed_dict.update(
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reward_signal.prepare_update(self.policy, batch, num_sequences)
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)
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stats_needed.update(reward_signal.stats_name_to_update_name)
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update_vals = self._execute_model(feed_dict, self.update_dict)
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for stat_name, update_name in stats_needed.items():
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update_stats[stat_name] = update_vals[update_name]
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return update_stats
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def _construct_feed_dict(
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self, mini_batch: AgentBuffer, num_sequences: int
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) -> Dict[tf.Tensor, Any]:
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# Do an optional burn-in for memories
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num_burn_in = int(self.burn_in_ratio * self.policy.sequence_length)
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burn_in_mask = np.ones((self.policy.sequence_length), dtype=np.float32)
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burn_in_mask[range(0, num_burn_in)] = 0
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burn_in_mask = np.tile(burn_in_mask, num_sequences)
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feed_dict = {
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self.policy.batch_size_ph: num_sequences,
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self.policy.sequence_length_ph: self.policy.sequence_length,
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self.policy.mask_input: mini_batch["masks"] * burn_in_mask,
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self.advantage: mini_batch["advantages"],
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self.all_old_log_probs: mini_batch["action_probs"],
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}
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for name in self.reward_signals:
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feed_dict[self.returns_holders[name]] = mini_batch[f"{name}_returns"]
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feed_dict[self.old_values[name]] = mini_batch[f"{name}_value_estimates"]
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if self.policy.output_pre is not None and "actions_pre" in mini_batch:
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feed_dict[self.policy.output_pre] = mini_batch["actions_pre"]
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else:
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feed_dict[self.policy.output] = mini_batch["actions"]
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if self.policy.use_recurrent:
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feed_dict[self.policy.prev_action] = mini_batch["prev_action"]
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feed_dict[self.policy.action_masks] = mini_batch["action_mask"]
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if "vector_obs" in mini_batch:
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feed_dict[self.policy.vector_in] = mini_batch["vector_obs"]
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if self.policy.vis_obs_size > 0:
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for i, _ in enumerate(self.policy.visual_in):
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feed_dict[self.policy.visual_in[i]] = mini_batch["visual_obs%d" % i]
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if self.policy.use_recurrent:
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feed_dict[self.policy.memory_in] = [
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mini_batch["memory"][i]
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for i in range(
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0, len(mini_batch["memory"]), self.policy.sequence_length
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)
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]
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feed_dict[self.memory_in] = self._make_zero_mem(
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self.m_size, mini_batch.num_experiences
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)
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return feed_dict
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