Andrew Cohen
5 年前
当前提交
704d0d11
共有 1 个文件被更改，包括 684 次插入 和 0 次删除
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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_envs.logging_util import get_logger |
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from mlagents.trainers.sac.network import SACPolicyNetwork, SACTargetNetwork |
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from mlagents.trainers.models import ScheduleType, EncoderType, ModelUtils |
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from mlagents.trainers.optimizer.tf_optimizer import TFOptimizer |
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from mlagents.trainers.policy.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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EPSILON = 1e-6 # Small value to avoid divide by zero |
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logger = get_logger(__name__) |
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POLICY_SCOPE = "" |
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TARGET_SCOPE = "target_network" |
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class MEDEOptimizer(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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# 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(""): |
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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 = ScheduleType( |
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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.burn_in_ratio = float(trainer_params.get("burn_in_ratio", 0.0)) |
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self.num_diverse = int(trainer_params.get("mede", 10)) |
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# Non-exposed SAC parameters |
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self.discrete_target_entropy_scale = ( |
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0.2 |
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) # Roughly equal to e-greedy 0.05 |
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self.continuous_target_entropy_scale = 1.0 |
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self.init_entcoef = trainer_params.get("init_entcoef", 1.0) |
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stream_names = list(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_disc: Optional[tf.Operation] = None |
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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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obs, self._z_one_hot = self._split(self.policy.vector_in) |
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self.disc = ModelUtils.create_discriminator( |
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obs, |
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self.num_diverse, |
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action_input=self.policy_network.external_action_in, |
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) |
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self.discp = None |
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if self.policy.use_continuous_act: |
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self.discp = ModelUtils.create_discriminator( |
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obs, |
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self.num_diverse, |
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action_input=self.policy.output, |
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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 = ModelUtils.create_schedule( |
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lr_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_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_optimizer_ops() |
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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.policy.initialize_or_load() |
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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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"Losses/Discriminator Loss": "disc_loss", |
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"Policy/Entropy Coeff": "entropy_coef", |
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"Policy/Learning Rate": "learning_rate", |
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"Policy/Discriminability": "discriminability", |
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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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"disc_loss": self.disc_loss, |
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"discriminability": self.discriminability, |
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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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"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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"update_disc": self.update_batch_disc, |
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"learning_rate": self.learning_rate, |
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} |
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def _split(self, observation_and_skill: tf.Tensor) -> List[tf.Tensor]: |
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return tf.split(observation_and_skill, [self.policy.vec_obs_size - self.num_diverse, self.num_diverse], 1) |
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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.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. |
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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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self.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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* self.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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#discriminator loss |
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self.disc_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self._z_one_hot, logits=self.disc)) |
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discriminabilityp = None |
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if self.policy.use_continuous_act: |
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discriminabilityp = -1 * tf.nn.softmax_cross_entropy_with_logits(labels=self._z_one_hot, logits=self.discp) |
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self.discriminability = -1 * tf.nn.softmax_cross_entropy_with_logits(labels=self._z_one_hot, logits=self.disc) |
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for name in stream_names: |
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if discrete: |
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_branched_mpq1 = ModelUtils.break_into_branches( |
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self.policy_network.q1_pheads[name] * discrete_action_probs, |
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self.act_size, |
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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 = ModelUtils.break_into_branches( |
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self.policy_network.q2_pheads[name] * discrete_action_probs, |
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self.act_size, |
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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 = ModelUtils.break_into_branches( |
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self.policy.selected_actions * q1_streams[name], self.act_size |
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) |
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branched_q2_stream = ModelUtils.break_into_branches( |
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self.policy.selected_actions * q2_streams[name], self.act_size |
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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 = ModelUtils.break_into_branches( |
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per_action_entropy, self.act_size |
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) |
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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 = ModelUtils.break_into_branches( |
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discrete_action_probs * self.policy_network.q1_p, self.act_size |
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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) - self.discriminability |
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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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+ self.discriminability |
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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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self.policy.all_log_probs + 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 * self.policy.all_log_probs - self.policy_network.q1_p, |
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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 - discriminabilityp |
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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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+ self.discriminability |
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- tf.reduce_sum(self.ent_coef * self.policy.all_log_probs, 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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|
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self.total_value_loss = self.q1_loss + self.q2_loss + self.value_loss |
|||
|
|||
self.entropy = self.policy_network.entropy |
|||
|
|||
def _create_sac_optimizer_ops(self) -> None: |
|||
""" |
|||
Creates the Adam optimizers and update ops for SAC, including |
|||
the policy, value, and entropy updates, as well as the target network update. |
|||
""" |
|||
policy_optimizer = self.create_optimizer_op( |
|||
learning_rate=self.learning_rate, name="sac_policy_opt" |
|||
) |
|||
entropy_optimizer = self.create_optimizer_op( |
|||
learning_rate=self.learning_rate, name="sac_entropy_opt" |
|||
) |
|||
value_optimizer = self.create_optimizer_op( |
|||
learning_rate=self.learning_rate, name="sac_value_opt" |
|||
) |
|||
|
|||
discriminator_optimizer = self.create_optimizer_op( |
|||
learning_rate=self.learning_rate, name="mede_disc_opt" |
|||
) |
|||
|
|||
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") |
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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 = self.policy.get_trainable_variables() |
|||
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 |
|||
) |
|||
] |
|||
|
|||
discriminator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="discriminator") |
|||
|
|||
self.update_batch_disc = discriminator_optimizer.minimize( |
|||
self.disc_loss, var_list=discriminator_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, AgentBuffer], 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, AgentBuffer], |
|||
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. |
|||
""" |
|||
# Do an optional burn-in for memories |
|||
num_burn_in = int(self.burn_in_ratio * self.policy.sequence_length) |
|||
burn_in_mask = np.ones((self.policy.sequence_length), dtype=np.float32) |
|||
burn_in_mask[range(0, num_burn_in)] = 0 |
|||
burn_in_mask = np.tile(burn_in_mask, num_sequences) |
|||
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"] * burn_in_mask, |
|||
} |
|||
for name in self.reward_signals: |
|||
feed_dict[self.rewards_holders[name]] = batch["{}_rewards".format(name)] |
|||
|
|||
if self.policy.use_continuous_act: |
|||
feed_dict[self.policy_network.external_action_in] = batch["actions"] |
|||
else: |
|||
# for discriminator |
|||
feed_dict[self.policy_network.external_action_in] = batch["actions"] |
|||
feed_dict[policy.output] = 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] = [ |
|||
batch["memory"][i] |
|||
for i in range(0, len(batch["memory"]), self.policy.sequence_length) |
|||
] |
|||
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.m_size // 3, batch.num_experiences |
|||
) |
|||
feed_dict[self.dones_holder] = batch["done"] |
|||
return feed_dict |
|||
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