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314 行
13 KiB
314 行
13 KiB
import logging
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from typing import Dict, Any, Optional
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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.brain import BrainInfo, BrainParameters
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from mlagents.trainers.models import EncoderType, LearningRateSchedule
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from mlagents.trainers.sac.models import SACModel
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from mlagents.trainers.tf_policy import TFPolicy
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from mlagents.trainers.components.reward_signals.reward_signal_factory import (
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create_reward_signal,
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)
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from mlagents.trainers.components.reward_signals import RewardSignal
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from mlagents.trainers.components.bc.module import BCModule
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logger = logging.getLogger("mlagents.trainers")
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class SACPolicy(TFPolicy):
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def __init__(
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self,
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seed: int,
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brain: BrainParameters,
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trainer_params: Dict[str, Any],
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is_training: bool,
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load: bool,
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) -> None:
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"""
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Policy for Proximal Policy Optimization Networks.
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:param seed: Random seed.
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:param brain: Assigned Brain object.
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:param trainer_params: Defined training parameters.
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:param is_training: Whether the model should be trained.
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:param load: Whether a pre-trained model will be loaded or a new one created.
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"""
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super().__init__(seed, brain, trainer_params)
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reward_signal_configs = {}
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for key, rsignal in trainer_params["reward_signals"].items():
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if type(rsignal) is dict:
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reward_signal_configs[key] = rsignal
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self.inference_dict: Dict[str, tf.Tensor] = {}
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self.update_dict: Dict[str, tf.Tensor] = {}
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self.create_model(
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brain, trainer_params, reward_signal_configs, is_training, load, seed
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)
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self.create_reward_signals(reward_signal_configs)
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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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with self.graph.as_default():
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# Create pretrainer if needed
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self.bc_module: Optional[BCModule] = None
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if "behavioral_cloning" in trainer_params:
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BCModule.check_config(trainer_params["behavioral_cloning"])
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self.bc_module = BCModule(
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self,
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policy_learning_rate=trainer_params["learning_rate"],
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default_batch_size=trainer_params["batch_size"],
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default_num_epoch=1,
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samples_per_update=trainer_params["batch_size"],
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**trainer_params["behavioral_cloning"],
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)
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# SAC-specific setting - we don't want to do a whole epoch each update!
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if "samples_per_update" in trainer_params["behavioral_cloning"]:
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logger.warning(
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"Pretraining: Samples Per Update is not a valid setting for SAC."
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)
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self.bc_module.samples_per_update = 1
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if load:
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self._load_graph()
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else:
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self._initialize_graph()
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self.sess.run(self.model.target_init_op)
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# Disable terminal states for certain reward signals to avoid survivor bias
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for name, reward_signal in self.reward_signals.items():
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if not reward_signal.use_terminal_states:
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self.sess.run(self.model.disable_use_dones[name])
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def create_model(
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self,
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brain: BrainParameters,
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trainer_params: Dict[str, Any],
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reward_signal_configs: Dict[str, Any],
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is_training: bool,
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load: bool,
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seed: int,
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) -> None:
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with self.graph.as_default():
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self.model = SACModel(
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brain,
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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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h_size=int(trainer_params["hidden_units"]),
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init_entcoef=float(trainer_params["init_entcoef"]),
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max_step=float(trainer_params["max_steps"]),
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normalize=trainer_params["normalize"],
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use_recurrent=trainer_params["use_recurrent"],
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num_layers=int(trainer_params["num_layers"]),
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m_size=self.m_size,
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seed=seed,
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stream_names=list(reward_signal_configs.keys()),
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tau=float(trainer_params["tau"]),
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gammas=[_val["gamma"] for _val in reward_signal_configs.values()],
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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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)
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self.model.create_sac_optimizers()
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self.inference_dict.update(
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{
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"action": self.model.output,
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"log_probs": self.model.all_log_probs,
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"value_heads": self.model.value_heads,
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"value": self.model.value,
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"entropy": self.model.entropy,
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"learning_rate": self.model.learning_rate,
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}
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)
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if self.use_continuous_act:
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self.inference_dict["pre_action"] = self.model.output_pre
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if self.use_recurrent:
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self.inference_dict["memory_out"] = self.model.memory_out
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self.update_dict.update(
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{
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"value_loss": self.model.total_value_loss,
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"policy_loss": self.model.policy_loss,
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"q1_loss": self.model.q1_loss,
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"q2_loss": self.model.q2_loss,
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"entropy_coef": self.model.ent_coef,
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"entropy": self.model.entropy,
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"update_batch": self.model.update_batch_policy,
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"update_value": self.model.update_batch_value,
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"update_entropy": self.model.update_batch_entropy,
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}
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)
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def create_reward_signals(self, reward_signal_configs: Dict[str, Any]) -> None:
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"""
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Create reward signals
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:param reward_signal_configs: Reward signal config.
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"""
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self.reward_signals: Dict[str, RewardSignal] = {}
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with self.graph.as_default():
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# Create reward signals
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for reward_signal, config in reward_signal_configs.items():
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if type(config) is dict:
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self.reward_signals[reward_signal] = create_reward_signal(
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self, self.model, reward_signal, config
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)
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def evaluate(self, brain_info: BrainInfo) -> Dict[str, np.ndarray]:
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"""
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Evaluates policy for the agent experiences provided.
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:param brain_info: BrainInfo object containing inputs.
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:return: Outputs from network as defined by self.inference_dict.
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"""
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feed_dict = {
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self.model.batch_size: len(brain_info.vector_observations),
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self.model.sequence_length: 1,
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}
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if self.use_recurrent:
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if not self.use_continuous_act:
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feed_dict[self.model.prev_action] = self.retrieve_previous_action(
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brain_info.agents
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)
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feed_dict[self.model.memory_in] = self.retrieve_memories(brain_info.agents)
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feed_dict = self.fill_eval_dict(feed_dict, brain_info)
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run_out = self._execute_model(feed_dict, self.inference_dict)
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return run_out
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@timed
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def update(
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self, mini_batch: Dict[str, Any], num_sequences: int
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) -> Dict[str, float]:
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"""
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Updates model using buffer.
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:param num_sequences: Number of trajectories in batch.
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:param mini_batch: Experience batch.
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:param update_target: Whether or not to update target value network
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:param reward_signal_mini_batches: Minibatches to use for updating the reward signals,
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indexed by name. If none, don't update the reward signals.
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:return: Output from update process.
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"""
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feed_dict = self.construct_feed_dict(self.model, mini_batch, num_sequences)
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stats_needed = self.stats_name_to_update_name
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update_stats: Dict[str, float] = {}
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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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# Update target network. By default, target update happens at every policy update.
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self.sess.run(self.model.target_update_op)
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return update_stats
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def update_reward_signals(
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self, reward_signal_minibatches: Dict[str, Dict], num_sequences: int
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) -> Dict[str, float]:
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"""
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Only update the reward signals.
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:param reward_signal_mini_batches: Minibatches to use for updating the reward signals,
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indexed by name. If none, don't update the reward signals.
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"""
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# Collect feed dicts for all reward signals.
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feed_dict: Dict[tf.Tensor, Any] = {}
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update_dict: Dict[str, tf.Tensor] = {}
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update_stats: Dict[str, float] = {}
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stats_needed: Dict[str, str] = {}
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if reward_signal_minibatches:
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self.add_reward_signal_dicts(
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feed_dict,
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update_dict,
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stats_needed,
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reward_signal_minibatches,
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num_sequences,
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)
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update_vals = self._execute_model(feed_dict, 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 add_reward_signal_dicts(
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self,
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feed_dict: Dict[tf.Tensor, Any],
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update_dict: Dict[str, tf.Tensor],
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stats_needed: Dict[str, str],
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reward_signal_minibatches: Dict[str, Dict],
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num_sequences: int,
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) -> None:
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"""
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Adds the items needed for reward signal updates to the feed_dict and stats_needed dict.
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:param feed_dict: Feed dict needed update
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:param update_dit: Update dict that needs update
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:param stats_needed: Stats needed to get from the update.
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:param reward_signal_minibatches: Minibatches to use for updating the reward signals,
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indexed by name.
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"""
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for name, r_mini_batch in reward_signal_minibatches.items():
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feed_dict.update(
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self.reward_signals[name].prepare_update(
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self.model, r_mini_batch, num_sequences
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)
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)
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update_dict.update(self.reward_signals[name].update_dict)
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stats_needed.update(self.reward_signals[name].stats_name_to_update_name)
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def construct_feed_dict(
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self, model: SACModel, mini_batch: Dict[str, Any], num_sequences: int
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) -> Dict[tf.Tensor, Any]:
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"""
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Builds the feed dict for updating the SAC model.
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:param model: The model to update. May be different when, e.g. using multi-GPU.
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:param mini_batch: Mini-batch to use to update.
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:param num_sequences: Number of LSTM sequences in mini_batch.
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"""
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feed_dict = {
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self.model.batch_size: num_sequences,
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self.model.sequence_length: self.sequence_length,
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self.model.next_sequence_length: self.sequence_length,
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self.model.mask_input: mini_batch["masks"],
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}
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for name in self.reward_signals:
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feed_dict[model.rewards_holders[name]] = mini_batch[
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"{}_rewards".format(name)
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]
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if self.use_continuous_act:
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feed_dict[model.action_holder] = mini_batch["actions"]
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else:
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feed_dict[model.action_holder] = mini_batch["actions"]
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if self.use_recurrent:
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feed_dict[model.prev_action] = mini_batch["prev_action"]
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feed_dict[model.action_masks] = mini_batch["action_mask"]
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if self.use_vec_obs:
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feed_dict[model.vector_in] = mini_batch["vector_obs"]
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feed_dict[model.next_vector_in] = mini_batch["next_vector_in"]
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if self.model.vis_obs_size > 0:
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for i, _ in enumerate(model.visual_in):
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_obs = mini_batch["visual_obs%d" % i]
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feed_dict[model.visual_in[i]] = _obs
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for i, _ in enumerate(model.next_visual_in):
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_obs = mini_batch["next_visual_obs%d" % i]
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feed_dict[model.next_visual_in[i]] = _obs
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if self.use_recurrent:
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mem_in = [
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mini_batch["memory"][i]
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for i in range(0, len(mini_batch["memory"]), self.sequence_length)
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]
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# LSTM shouldn't have sequence length <1, but stop it from going out of the index if true.
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offset = 1 if self.sequence_length > 1 else 0
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next_mem_in = [
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mini_batch["memory"][i][
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: self.m_size // 4
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] # only pass value part of memory to target network
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for i in range(offset, len(mini_batch["memory"]), self.sequence_length)
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]
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feed_dict[model.memory_in] = mem_in
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feed_dict[model.next_memory_in] = next_mem_in
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feed_dict[model.dones_holder] = mini_batch["done"]
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return feed_dict
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