ml-agents/ml-agents/mlagents/trainers/sac/policy.py

import logging
from typing import Dict, Any, Optional
import numpy as np
from mlagents.trainers import tf

from mlagents.envs.timers import timed
from mlagents.envs.brain import BrainInfo, BrainParameters
from mlagents.trainers.models import EncoderType, LearningRateSchedule
from mlagents.trainers.sac.models import SACModel
from mlagents.trainers.tf_policy import TFPolicy
from mlagents.trainers.components.reward_signals.reward_signal_factory import (
    create_reward_signal,
)
from mlagents.trainers.components.reward_signals import RewardSignal
from mlagents.trainers.components.bc.module import BCModule

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


class SACPolicy(TFPolicy):
    def __init__(
        self,
        seed: int,
        brain: BrainParameters,
        trainer_params: Dict[str, Any],
        is_training: bool,
        load: bool,
    ) -> None:
        """
        Policy for Proximal Policy Optimization Networks.
        :param seed: Random seed.
        :param brain: Assigned Brain object.
        :param trainer_params: Defined training parameters.
        :param is_training: Whether the model should be trained.
        :param load: Whether a pre-trained model will be loaded or a new one created.
        """
        super().__init__(seed, brain, trainer_params)

        reward_signal_configs = {}
        for key, rsignal in trainer_params["reward_signals"].items():
            if type(rsignal) is dict:
                reward_signal_configs[key] = rsignal

        self.inference_dict: Dict[str, tf.Tensor] = {}
        self.update_dict: Dict[str, tf.Tensor] = {}
        self.create_model(
            brain, trainer_params, reward_signal_configs, is_training, load, seed
        )
        self.create_reward_signals(reward_signal_configs)

        self.stats_name_to_update_name = {
            "Losses/Value Loss": "value_loss",
            "Losses/Policy Loss": "policy_loss",
            "Losses/Q1 Loss": "q1_loss",
            "Losses/Q2 Loss": "q2_loss",
            "Policy/Entropy Coeff": "entropy_coef",
        }

        with self.graph.as_default():
            # Create pretrainer if needed
            self.bc_module: Optional[BCModule] = None
            if "pretraining" in trainer_params:
                BCModule.check_config(trainer_params["pretraining"])
                self.bc_module = BCModule(
                    self,
                    policy_learning_rate=trainer_params["learning_rate"],
                    default_batch_size=trainer_params["batch_size"],
                    default_num_epoch=1,
                    samples_per_update=trainer_params["batch_size"],
                    **trainer_params["pretraining"],
                )
                # SAC-specific setting - we don't want to do a whole epoch each update!
                if "samples_per_update" in trainer_params["pretraining"]:
                    logger.warning(
                        "Pretraining: Samples Per Update is not a valid setting for SAC."
                    )
                    self.bc_module.samples_per_update = 1

        if load:
            self._load_graph()
        else:
            self._initialize_graph()
            self.sess.run(self.model.target_init_op)

        # Disable terminal states for certain reward signals to avoid survivor bias
        for name, reward_signal in self.reward_signals.items():
            if not reward_signal.use_terminal_states:
                self.sess.run(self.model.disable_use_dones[name])

    def create_model(
        self,
        brain: BrainParameters,
        trainer_params: Dict[str, Any],
        reward_signal_configs: Dict[str, Any],
        is_training: bool,
        load: bool,
        seed: int,
    ) -> None:
        with self.graph.as_default():
            self.model = SACModel(
                brain,
                lr=float(trainer_params["learning_rate"]),
                lr_schedule=LearningRateSchedule(
                    trainer_params.get("learning_rate_schedule", "constant")
                ),
                h_size=int(trainer_params["hidden_units"]),
                init_entcoef=float(trainer_params["init_entcoef"]),
                max_step=float(trainer_params["max_steps"]),
                normalize=trainer_params["normalize"],
                use_recurrent=trainer_params["use_recurrent"],
                num_layers=int(trainer_params["num_layers"]),
                m_size=self.m_size,
                seed=seed,
                stream_names=list(reward_signal_configs.keys()),
                tau=float(trainer_params["tau"]),
                gammas=list(_val["gamma"] for _val in reward_signal_configs.values()),
                vis_encode_type=EncoderType(
                    trainer_params.get("vis_encode_type", "simple")
                ),
            )
            self.model.create_sac_optimizers()

        self.inference_dict.update(
            {
                "action": self.model.output,
                "log_probs": self.model.all_log_probs,
                "value_heads": self.model.value_heads,
                "value": self.model.value,
                "entropy": self.model.entropy,
                "learning_rate": self.model.learning_rate,
            }
        )
        if self.use_continuous_act:
            self.inference_dict["pre_action"] = self.model.output_pre
        if self.use_recurrent:
            self.inference_dict["memory_out"] = self.model.memory_out

        self.update_dict.update(
            {
                "value_loss": self.model.total_value_loss,
                "policy_loss": self.model.policy_loss,
                "q1_loss": self.model.q1_loss,
                "q2_loss": self.model.q2_loss,
                "entropy_coef": self.model.ent_coef,
                "entropy": self.model.entropy,
                "update_batch": self.model.update_batch_policy,
                "update_value": self.model.update_batch_value,
                "update_entropy": self.model.update_batch_entropy,
            }
        )

    def create_reward_signals(self, reward_signal_configs: Dict[str, Any]) -> None:
        """
        Create reward signals
        :param reward_signal_configs: Reward signal config.
        """
        self.reward_signals: Dict[str, RewardSignal] = {}
        with self.graph.as_default():
            # Create reward signals
            for reward_signal, config in reward_signal_configs.items():
                if type(config) is dict:
                    self.reward_signals[reward_signal] = create_reward_signal(
                        self, self.model, reward_signal, config
                    )

    def evaluate(self, brain_info: BrainInfo) -> Dict[str, np.ndarray]:
        """
        Evaluates policy for the agent experiences provided.
        :param brain_info: BrainInfo object containing inputs.
        :return: Outputs from network as defined by self.inference_dict.
        """
        feed_dict = {
            self.model.batch_size: len(brain_info.vector_observations),
            self.model.sequence_length: 1,
        }
        if self.use_recurrent:
            if not self.use_continuous_act:
                feed_dict[
                    self.model.prev_action
                ] = brain_info.previous_vector_actions.reshape(
                    [-1, len(self.model.act_size)]
                )
            if brain_info.memories.shape[1] == 0:
                brain_info.memories = self.make_empty_memory(len(brain_info.agents))
            feed_dict[self.model.memory_in] = brain_info.memories

        feed_dict = self.fill_eval_dict(feed_dict, brain_info)
        run_out = self._execute_model(feed_dict, self.inference_dict)
        return run_out

    @timed
    def update(
        self, mini_batch: Dict[str, Any], num_sequences: int, update_target: bool = True
    ) -> Dict[str, float]:
        """
        Updates model using buffer.
        :param num_sequences: Number of trajectories in batch.
        :param mini_batch: Experience batch.
        :param update_target: Whether or not to update target value network
        :param reward_signal_mini_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.model, mini_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]
        if update_target:
            self.sess.run(self.model.target_update_op)
        return update_stats

    def update_reward_signals(
        self, reward_signal_minibatches: Dict[str, Dict], num_sequences: int
    ) -> Dict[str, float]:
        """
        Only update the reward signals.
        :param reward_signal_mini_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: Dict[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_mini_batch in reward_signal_minibatches.items():
            feed_dict.update(
                self.reward_signals[name].prepare_update(
                    self.model, r_mini_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, model: SACModel, mini_batch: Dict[str, Any], 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 mini_batch: Mini-batch to use to update.
        :param num_sequences: Number of LSTM sequences in mini_batch.
        """
        feed_dict = {
            self.model.batch_size: num_sequences,
            self.model.sequence_length: self.sequence_length,
            self.model.next_sequence_length: self.sequence_length,
            self.model.mask_input: mini_batch["masks"],
        }
        for name in self.reward_signals:
            feed_dict[model.rewards_holders[name]] = mini_batch[
                "{}_rewards".format(name)
            ]

        if self.use_continuous_act:
            feed_dict[model.action_holder] = mini_batch["actions"]
        else:
            feed_dict[model.action_holder] = mini_batch["actions"]
            if self.use_recurrent:
                feed_dict[model.prev_action] = mini_batch["prev_action"]
            feed_dict[model.action_masks] = mini_batch["action_mask"]
        if self.use_vec_obs:
            feed_dict[model.vector_in] = mini_batch["vector_obs"]
            feed_dict[model.next_vector_in] = mini_batch["next_vector_in"]
        if self.model.vis_obs_size > 0:
            for i, _ in enumerate(model.visual_in):
                _obs = mini_batch["visual_obs%d" % i]
                feed_dict[model.visual_in[i]] = _obs
            for i, _ in enumerate(model.next_visual_in):
                _obs = mini_batch["next_visual_obs%d" % i]
                feed_dict[model.next_visual_in[i]] = _obs
        if self.use_recurrent:
            mem_in = [
                mini_batch["memory"][i]
                for i in range(0, len(mini_batch["memory"]), self.sequence_length)
            ]
            # LSTM shouldn't have sequence length <1, but stop it from going out of the index if true.
            offset = 1 if self.sequence_length > 1 else 0
            next_mem_in = [
                mini_batch["memory"][i][
                    : self.m_size // 4
                ]  # only pass value part of memory to target network
                for i in range(offset, len(mini_batch["memory"]), self.sequence_length)
            ]
            feed_dict[model.memory_in] = mem_in
            feed_dict[model.next_memory_in] = next_mem_in
        feed_dict[model.dones_holder] = mini_batch["done"]
        return feed_dict