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625 行
26 KiB
625 行
26 KiB
import logging
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import numpy as np
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from typing import Dict, List, Optional, Any, Mapping
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from mlagents.tf_utils import tf
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from mlagents.trainers.sac.network import SACPolicyNetwork, SACTargetNetwork
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from mlagents.trainers.models import LearningRateSchedule, EncoderType, LearningModel
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from mlagents.trainers.optimizer import TFOptimizer
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from mlagents.trainers.tf_policy import TFPolicy
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from mlagents.trainers.buffer import AgentBuffer
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from mlagents_envs.timers import timed
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LOG_STD_MAX = 2
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LOG_STD_MIN = -20
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EPSILON = 1e-6 # Small value to avoid divide by zero
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DISCRETE_TARGET_ENTROPY_SCALE = 0.2 # Roughly equal to e-greedy 0.05
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CONTINUOUS_TARGET_ENTROPY_SCALE = 1.0 # TODO: Make these an optional hyperparam.
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LOGGER = logging.getLogger("mlagents.trainers")
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POLICY_SCOPE = ""
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TARGET_SCOPE = "target_network"
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class SACOptimizer(TFOptimizer):
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def __init__(self, policy: TFPolicy, trainer_params: Dict[str, Any]):
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"""
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Takes a Unity environment and model-specific hyper-parameters and returns the
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appropriate PPO agent model for the environment.
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:param brain: Brain parameters used to generate specific network graph.
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:param lr: Learning rate.
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:param lr_schedule: Learning rate decay schedule.
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:param h_size: Size of hidden layers
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:param init_entcoef: Initial value for entropy coefficient. Set lower to learn faster,
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set higher to explore more.
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:return: a sub-class of PPOAgent tailored to the environment.
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:param max_step: Total number of training steps.
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:param normalize: Whether to normalize vector observation input.
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:param use_recurrent: Whether to use an LSTM layer in the network.
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:param num_layers: Number of hidden layers between encoded input and policy & value layers
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:param tau: Strength of soft-Q update.
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:param m_size: Size of brain memory.
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"""
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with policy.graph.as_default():
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with tf.variable_scope(""):
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super().__init__(policy, trainer_params)
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lr = float(trainer_params["learning_rate"])
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lr_schedule = LearningRateSchedule(
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trainer_params.get("learning_rate_schedule", "constant")
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)
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self.policy = policy
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self.act_size = self.policy.act_size
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h_size = int(trainer_params["hidden_units"])
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max_step = float(trainer_params["max_steps"])
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num_layers = int(trainer_params["num_layers"])
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vis_encode_type = EncoderType(
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trainer_params.get("vis_encode_type", "simple")
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)
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self.tau = trainer_params.get("tau", 0.005)
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self.init_entcoef = trainer_params.get("init_entcoef", 1.0)
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stream_names = self.reward_signals.keys()
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# Use to reduce "survivor bonus" when using Curiosity or GAIL.
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self.gammas = [
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_val["gamma"] for _val in trainer_params["reward_signals"].values()
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]
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self.use_dones_in_backup = {
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name: tf.Variable(1.0) for name in stream_names
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}
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self.disable_use_dones = {
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name: self.use_dones_in_backup[name].assign(0.0)
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for name in stream_names
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}
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if num_layers < 1:
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num_layers = 1
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self.target_init_op: List[tf.Tensor] = []
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self.target_update_op: List[tf.Tensor] = []
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self.update_batch_policy: Optional[tf.Operation] = None
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self.update_batch_value: Optional[tf.Operation] = None
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self.update_batch_entropy: Optional[tf.Operation] = None
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self.policy_network = SACPolicyNetwork(
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policy=self.policy,
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m_size=self.policy.m_size, # 3x policy.m_size
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h_size=h_size,
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normalize=self.policy.normalize,
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use_recurrent=self.policy.use_recurrent,
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num_layers=num_layers,
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stream_names=stream_names,
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vis_encode_type=vis_encode_type,
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)
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self.target_network = SACTargetNetwork(
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policy=self.policy,
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m_size=self.policy.m_size, # 1x policy.m_size
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h_size=h_size,
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normalize=self.policy.normalize,
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use_recurrent=self.policy.use_recurrent,
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num_layers=num_layers,
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stream_names=stream_names,
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vis_encode_type=vis_encode_type,
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)
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# The optimizer's m_size is 3 times the policy (Q1, Q2, and Value)
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self.m_size = 3 * self.policy.m_size
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self.create_inputs_and_outputs()
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self.learning_rate = LearningModel.create_learning_rate(
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lr_schedule, lr, self.policy.global_step, int(max_step)
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)
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self.create_losses(
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self.policy_network.q1_heads,
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self.policy_network.q2_heads,
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lr,
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int(max_step),
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stream_names,
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discrete=not self.policy.use_continuous_act,
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)
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self.create_sac_optimizers()
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self.selected_actions = (
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self.policy.selected_actions
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) # For GAIL and other reward signals
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if self.policy.normalize:
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target_update_norm = self.target_network.copy_normalization(
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self.policy.running_mean,
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self.policy.running_variance,
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self.policy.normalization_steps,
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)
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# Update the normalization of the optimizer when the policy does.
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self.policy.update_normalization_op = tf.group(
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[self.policy.update_normalization_op, target_update_norm]
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)
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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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"Losses/Q1 Loss": "q1_loss",
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"Losses/Q2 Loss": "q2_loss",
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"Policy/Entropy Coeff": "entropy_coef",
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}
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self.update_dict = {
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"value_loss": self.total_value_loss,
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"policy_loss": self.policy_loss,
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"q1_loss": self.q1_loss,
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"q2_loss": self.q2_loss,
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"entropy_coef": self.ent_coef,
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"entropy": self.policy.entropy,
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"update_batch": self.update_batch_policy,
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"update_value": self.update_batch_value,
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"update_entropy": self.update_batch_entropy,
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}
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# Add some stuff to inference dict from optimizer
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self.policy.inference_dict["learning_rate"] = self.learning_rate
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def create_inputs_and_outputs(self) -> None:
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"""
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Assign the higher-level SACModel's inputs and outputs to those of its policy or
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target network.
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"""
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self.vector_in = self.policy.vector_in
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self.visual_in = self.policy.visual_in
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self.next_vector_in = self.target_network.vector_in
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self.next_visual_in = self.target_network.visual_in
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self.action_holder = self.policy.action_holder
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self.sequence_length_ph = self.policy.sequence_length_ph
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self.next_sequence_length_ph = self.target_network.sequence_length_ph
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if not self.policy.use_continuous_act:
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self.action_masks = self.policy_network.action_masks
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else:
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self.output_pre = self.policy_network.output_pre
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# Don't use value estimate during inference. TODO: Check why PPO uses value_estimate in inference.
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self.value = tf.identity(
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self.policy_network.value, name="value_estimate_unused"
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)
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self.value_heads = self.policy_network.value_heads
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self.dones_holder = tf.placeholder(
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shape=[None], dtype=tf.float32, name="dones_holder"
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)
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if self.policy.use_recurrent:
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self.memory_in = self.policy_network.memory_in
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self.memory_out = self.policy_network.memory_out
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if not self.policy.use_continuous_act:
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self.prev_action = self.policy_network.prev_action
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self.next_memory_in = self.target_network.memory_in
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def create_losses(
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self,
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q1_streams: Dict[str, tf.Tensor],
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q2_streams: Dict[str, tf.Tensor],
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lr: tf.Tensor,
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max_step: int,
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stream_names: List[str],
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discrete: bool = False,
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) -> None:
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"""
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Creates training-specific Tensorflow ops for SAC models.
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:param q1_streams: Q1 streams from policy network
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:param q1_streams: Q2 streams from policy network
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:param lr: Learning rate
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:param max_step: Total number of training steps.
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:param stream_names: List of reward stream names.
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:param discrete: Whether or not to use discrete action losses.
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"""
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if discrete:
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self.target_entropy = [
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DISCRETE_TARGET_ENTROPY_SCALE * np.log(i).astype(np.float32)
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for i in self.act_size
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]
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discrete_action_probs = tf.exp(self.policy.all_log_probs)
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per_action_entropy = discrete_action_probs * self.policy.all_log_probs
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else:
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self.target_entropy = (
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-1
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* CONTINUOUS_TARGET_ENTROPY_SCALE
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* np.prod(self.act_size[0]).astype(np.float32)
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)
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self.rewards_holders = {}
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self.min_policy_qs = {}
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for name in stream_names:
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if discrete:
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_branched_mpq1 = self.apply_as_branches(
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self.policy_network.q1_pheads[name] * discrete_action_probs
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)
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branched_mpq1 = tf.stack(
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[
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tf.reduce_sum(_br, axis=1, keep_dims=True)
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for _br in _branched_mpq1
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]
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)
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_q1_p_mean = tf.reduce_mean(branched_mpq1, axis=0)
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_branched_mpq2 = self.apply_as_branches(
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self.policy_network.q2_pheads[name] * discrete_action_probs
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)
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branched_mpq2 = tf.stack(
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[
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tf.reduce_sum(_br, axis=1, keep_dims=True)
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for _br in _branched_mpq2
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]
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)
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_q2_p_mean = tf.reduce_mean(branched_mpq2, axis=0)
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self.min_policy_qs[name] = tf.minimum(_q1_p_mean, _q2_p_mean)
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else:
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self.min_policy_qs[name] = tf.minimum(
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self.policy_network.q1_pheads[name],
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self.policy_network.q2_pheads[name],
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)
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rewards_holder = tf.placeholder(
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shape=[None], dtype=tf.float32, name="{}_rewards".format(name)
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)
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self.rewards_holders[name] = rewards_holder
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q1_losses = []
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q2_losses = []
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# Multiple q losses per stream
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expanded_dones = tf.expand_dims(self.dones_holder, axis=-1)
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for i, name in enumerate(stream_names):
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_expanded_rewards = tf.expand_dims(self.rewards_holders[name], axis=-1)
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q_backup = tf.stop_gradient(
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_expanded_rewards
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+ (1.0 - self.use_dones_in_backup[name] * expanded_dones)
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* self.gammas[i]
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* self.target_network.value_heads[name]
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)
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if discrete:
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# We need to break up the Q functions by branch, and update them individually.
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branched_q1_stream = self.apply_as_branches(
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self.policy.action_oh * q1_streams[name]
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)
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branched_q2_stream = self.apply_as_branches(
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self.policy.action_oh * q2_streams[name]
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)
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# Reduce each branch into scalar
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branched_q1_stream = [
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tf.reduce_sum(_branch, axis=1, keep_dims=True)
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for _branch in branched_q1_stream
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]
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branched_q2_stream = [
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tf.reduce_sum(_branch, axis=1, keep_dims=True)
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for _branch in branched_q2_stream
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]
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q1_stream = tf.reduce_mean(branched_q1_stream, axis=0)
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q2_stream = tf.reduce_mean(branched_q2_stream, axis=0)
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else:
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q1_stream = q1_streams[name]
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q2_stream = q2_streams[name]
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_q1_loss = 0.5 * tf.reduce_mean(
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tf.to_float(self.policy.mask)
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* tf.squared_difference(q_backup, q1_stream)
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)
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_q2_loss = 0.5 * tf.reduce_mean(
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tf.to_float(self.policy.mask)
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* tf.squared_difference(q_backup, q2_stream)
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)
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q1_losses.append(_q1_loss)
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q2_losses.append(_q2_loss)
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self.q1_loss = tf.reduce_mean(q1_losses)
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self.q2_loss = tf.reduce_mean(q2_losses)
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# Learn entropy coefficient
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if discrete:
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# Create a log_ent_coef for each branch
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self.log_ent_coef = tf.get_variable(
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"log_ent_coef",
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dtype=tf.float32,
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initializer=np.log([self.init_entcoef] * len(self.act_size)).astype(
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np.float32
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),
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trainable=True,
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)
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else:
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self.log_ent_coef = tf.get_variable(
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"log_ent_coef",
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dtype=tf.float32,
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initializer=np.log(self.init_entcoef).astype(np.float32),
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trainable=True,
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)
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self.ent_coef = tf.exp(self.log_ent_coef)
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if discrete:
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# We also have to do a different entropy and target_entropy per branch.
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branched_per_action_ent = self.apply_as_branches(per_action_entropy)
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branched_ent_sums = tf.stack(
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[
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tf.reduce_sum(_lp, axis=1, keep_dims=True) + _te
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for _lp, _te in zip(branched_per_action_ent, self.target_entropy)
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],
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axis=1,
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)
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self.entropy_loss = -tf.reduce_mean(
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tf.to_float(self.policy.mask)
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* tf.reduce_mean(
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self.log_ent_coef
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* tf.squeeze(tf.stop_gradient(branched_ent_sums), axis=2),
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axis=1,
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)
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)
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# Same with policy loss, we have to do the loss per branch and average them,
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# so that larger branches don't get more weight.
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# The equivalent KL divergence from Eq 10 of Haarnoja et al. is also pi*log(pi) - Q
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branched_q_term = self.apply_as_branches(
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discrete_action_probs * self.policy_network.q1_p
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)
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branched_policy_loss = tf.stack(
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[
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tf.reduce_sum(self.ent_coef[i] * _lp - _qt, axis=1, keep_dims=True)
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for i, (_lp, _qt) in enumerate(
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zip(branched_per_action_ent, branched_q_term)
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)
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]
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)
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self.policy_loss = tf.reduce_mean(
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tf.to_float(self.policy.mask) * tf.squeeze(branched_policy_loss)
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)
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# Do vbackup entropy bonus per branch as well.
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branched_ent_bonus = tf.stack(
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[
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tf.reduce_sum(self.ent_coef[i] * _lp, axis=1, keep_dims=True)
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for i, _lp in enumerate(branched_per_action_ent)
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]
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)
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value_losses = []
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for name in stream_names:
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v_backup = tf.stop_gradient(
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self.min_policy_qs[name]
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- tf.reduce_mean(branched_ent_bonus, axis=0)
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)
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value_losses.append(
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0.5
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* tf.reduce_mean(
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tf.to_float(self.policy.mask)
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* tf.squared_difference(
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self.policy_network.value_heads[name], v_backup
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)
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)
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)
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else:
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self.entropy_loss = -tf.reduce_mean(
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self.log_ent_coef
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* tf.to_float(self.policy.mask)
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* tf.stop_gradient(
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tf.reduce_sum(
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branched_per_action_ent + self.target_entropy,
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axis=1,
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keep_dims=True,
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)
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)
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)
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batch_policy_loss = tf.reduce_mean(
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self.ent_coef * per_action_entropy - self.policy_network.q1_p, axis=1
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)
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self.policy_loss = tf.reduce_mean(
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tf.to_float(self.policy.mask) * batch_policy_loss
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)
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value_losses = []
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for name in stream_names:
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v_backup = tf.stop_gradient(
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self.min_policy_qs[name]
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- tf.reduce_sum(self.ent_coef * per_action_entropy, axis=1)
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)
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value_losses.append(
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0.5
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* tf.reduce_mean(
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tf.to_float(self.policy.mask)
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* tf.squared_difference(
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self.policy_network.value_heads[name], v_backup
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)
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)
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)
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self.value_loss = tf.reduce_mean(value_losses)
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self.total_value_loss = self.q1_loss + self.q2_loss + self.value_loss
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self.entropy = self.policy_network.entropy
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def apply_as_branches(self, concat_logits: tf.Tensor) -> List[tf.Tensor]:
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"""
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Takes in a concatenated set of logits and breaks it up into a list of non-concatenated logits, one per
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action branch
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"""
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action_idx = [0] + list(np.cumsum(self.act_size))
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branches_logits = [
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concat_logits[:, action_idx[i] : action_idx[i + 1]]
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for i in range(len(self.act_size))
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]
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return branches_logits
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def create_sac_optimizers(self) -> None:
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"""
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Creates the Adam optimizers and update ops for SAC, including
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the policy, value, and entropy updates, as well as the target network update.
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"""
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policy_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
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entropy_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
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value_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
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|
self.target_update_op = [
|
|
tf.assign(target, (1 - self.tau) * target + self.tau * source)
|
|
for target, source in zip(
|
|
self.target_network.value_vars, self.policy_network.value_vars
|
|
)
|
|
]
|
|
LOGGER.debug("value_vars")
|
|
self.print_all_vars(self.policy_network.value_vars)
|
|
LOGGER.debug("targvalue_vars")
|
|
self.print_all_vars(self.target_network.value_vars)
|
|
LOGGER.debug("critic_vars")
|
|
self.print_all_vars(self.policy_network.critic_vars)
|
|
LOGGER.debug("q_vars")
|
|
self.print_all_vars(self.policy_network.q_vars)
|
|
LOGGER.debug("policy_vars")
|
|
policy_vars = tf.get_collection(
|
|
tf.GraphKeys.TRAINABLE_VARIABLES, scope="policy"
|
|
)
|
|
self.print_all_vars(policy_vars)
|
|
|
|
self.target_init_op = [
|
|
tf.assign(target, source)
|
|
for target, source in zip(
|
|
self.target_network.value_vars, self.policy_network.value_vars
|
|
)
|
|
]
|
|
|
|
self.update_batch_policy = policy_optimizer.minimize(
|
|
self.policy_loss, var_list=policy_vars
|
|
)
|
|
|
|
# Make sure policy is updated first, then value, then entropy.
|
|
with tf.control_dependencies([self.update_batch_policy]):
|
|
self.update_batch_value = value_optimizer.minimize(
|
|
self.total_value_loss, var_list=self.policy_network.critic_vars
|
|
)
|
|
# Add entropy coefficient optimization operation
|
|
with tf.control_dependencies([self.update_batch_value]):
|
|
self.update_batch_entropy = entropy_optimizer.minimize(
|
|
self.entropy_loss, var_list=self.log_ent_coef
|
|
)
|
|
|
|
def print_all_vars(self, variables):
|
|
for _var in variables:
|
|
LOGGER.debug(_var)
|
|
|
|
@timed
|
|
def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
|
|
"""
|
|
Updates model using buffer.
|
|
:param num_sequences: Number of trajectories in batch.
|
|
:param batch: Experience mini-batch.
|
|
:param update_target: Whether or not to update target value network
|
|
:param reward_signal_batches: Minibatches to use for updating the reward signals,
|
|
indexed by name. If none, don't update the reward signals.
|
|
:return: Output from update process.
|
|
"""
|
|
feed_dict = self.construct_feed_dict(self.policy, batch, num_sequences)
|
|
stats_needed = self.stats_name_to_update_name
|
|
update_stats: Dict[str, float] = {}
|
|
update_vals = self._execute_model(feed_dict, self.update_dict)
|
|
for stat_name, update_name in stats_needed.items():
|
|
update_stats[stat_name] = update_vals[update_name]
|
|
# Update target network. By default, target update happens at every policy update.
|
|
self.sess.run(self.target_update_op)
|
|
return update_stats
|
|
|
|
def update_reward_signals(
|
|
self, reward_signal_minibatches: Mapping[str, Dict], num_sequences: int
|
|
) -> Dict[str, float]:
|
|
"""
|
|
Only update the reward signals.
|
|
:param reward_signal_batches: Minibatches to use for updating the reward signals,
|
|
indexed by name. If none, don't update the reward signals.
|
|
"""
|
|
# Collect feed dicts for all reward signals.
|
|
feed_dict: Dict[tf.Tensor, Any] = {}
|
|
update_dict: Dict[str, tf.Tensor] = {}
|
|
update_stats: Dict[str, float] = {}
|
|
stats_needed: Dict[str, str] = {}
|
|
if reward_signal_minibatches:
|
|
self.add_reward_signal_dicts(
|
|
feed_dict,
|
|
update_dict,
|
|
stats_needed,
|
|
reward_signal_minibatches,
|
|
num_sequences,
|
|
)
|
|
update_vals = self._execute_model(feed_dict, update_dict)
|
|
for stat_name, update_name in stats_needed.items():
|
|
update_stats[stat_name] = update_vals[update_name]
|
|
return update_stats
|
|
|
|
def add_reward_signal_dicts(
|
|
self,
|
|
feed_dict: Dict[tf.Tensor, Any],
|
|
update_dict: Dict[str, tf.Tensor],
|
|
stats_needed: Dict[str, str],
|
|
reward_signal_minibatches: Mapping[str, Dict],
|
|
num_sequences: int,
|
|
) -> None:
|
|
"""
|
|
Adds the items needed for reward signal updates to the feed_dict and stats_needed dict.
|
|
:param feed_dict: Feed dict needed update
|
|
:param update_dit: Update dict that needs update
|
|
:param stats_needed: Stats needed to get from the update.
|
|
:param reward_signal_minibatches: Minibatches to use for updating the reward signals,
|
|
indexed by name.
|
|
"""
|
|
for name, r_batch in reward_signal_minibatches.items():
|
|
feed_dict.update(
|
|
self.reward_signals[name].prepare_update(
|
|
self.policy, r_batch, num_sequences
|
|
)
|
|
)
|
|
update_dict.update(self.reward_signals[name].update_dict)
|
|
stats_needed.update(self.reward_signals[name].stats_name_to_update_name)
|
|
|
|
def construct_feed_dict(
|
|
self, policy: TFPolicy, batch: AgentBuffer, num_sequences: int
|
|
) -> Dict[tf.Tensor, Any]:
|
|
"""
|
|
Builds the feed dict for updating the SAC model.
|
|
:param model: The model to update. May be different when, e.g. using multi-GPU.
|
|
:param batch: Mini-batch to use to update.
|
|
:param num_sequences: Number of LSTM sequences in batch.
|
|
"""
|
|
feed_dict = {
|
|
policy.batch_size_ph: num_sequences,
|
|
policy.sequence_length_ph: self.policy.sequence_length,
|
|
self.next_sequence_length_ph: self.policy.sequence_length,
|
|
self.policy.mask_input: batch["masks"],
|
|
}
|
|
for name in self.reward_signals:
|
|
feed_dict[self.rewards_holders[name]] = batch["{}_rewards".format(name)]
|
|
|
|
if self.policy.use_continuous_act:
|
|
feed_dict[policy.action_holder] = batch["actions"]
|
|
else:
|
|
feed_dict[policy.action_holder] = batch["actions"]
|
|
if self.policy.use_recurrent:
|
|
feed_dict[policy.prev_action] = batch["prev_action"]
|
|
feed_dict[policy.action_masks] = batch["action_mask"]
|
|
if self.policy.use_vec_obs:
|
|
feed_dict[policy.vector_in] = batch["vector_obs"]
|
|
feed_dict[self.next_vector_in] = batch["next_vector_in"]
|
|
if self.policy.vis_obs_size > 0:
|
|
for i, _ in enumerate(policy.visual_in):
|
|
_obs = batch["visual_obs%d" % i]
|
|
feed_dict[policy.visual_in[i]] = _obs
|
|
for i, _ in enumerate(self.next_visual_in):
|
|
_obs = batch["next_visual_obs%d" % i]
|
|
feed_dict[self.next_visual_in[i]] = _obs
|
|
if self.policy.use_recurrent:
|
|
feed_dict[policy.memory_in] = self._make_zero_mem(
|
|
self.policy.m_size, batch.num_experiences
|
|
)
|
|
feed_dict[self.policy_network.memory_in] = self._make_zero_mem(
|
|
self.m_size, batch.num_experiences
|
|
)
|
|
feed_dict[self.target_network.memory_in] = self._make_zero_mem(
|
|
self.policy.m_size, batch.num_experiences
|
|
)
|
|
feed_dict[self.dones_holder] = batch["done"]
|
|
return feed_dict
|