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

import logging
import numpy as np
from typing import Any, Dict
import tensorflow as tf

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

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


class PPOPolicy(TFPolicy):
    def __init__(self, seed, brain, trainer_params, is_training, load):
        """
        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 = trainer_params["reward_signals"]
        self.inference_dict = {}
        self.update_dict = {}
        self.stats_name_to_update_name = {
            "Losses/Value Loss": "value_loss",
            "Losses/Policy Loss": "policy_loss",
        }

        self.create_model(
            brain, trainer_params, reward_signal_configs, is_training, load, seed
        )
        self.create_reward_signals(reward_signal_configs)

        with self.graph.as_default():
            # Create pretrainer if needed
            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=trainer_params["num_epoch"],
                    **trainer_params["pretraining"],
                )
            else:
                self.bc_module = None

        if load:
            self._load_graph()
        else:
            self._initialize_graph()

    def create_model(
        self, brain, trainer_params, reward_signal_configs, is_training, load, seed
    ):
        """
        Create PPO model
        :param brain: Assigned Brain object.
        :param trainer_params: Defined training parameters.
        :param reward_signal_configs: Reward signal config
        :param seed: Random seed.
        """
        with self.graph.as_default():
            self.model = PPOModel(
                brain=brain,
                lr=float(trainer_params["learning_rate"]),
                lr_schedule=LearningRateSchedule(
                    trainer_params.get("learning_rate_schedule", "linear")
                ),
                h_size=int(trainer_params["hidden_units"]),
                epsilon=float(trainer_params["epsilon"]),
                beta=float(trainer_params["beta"]),
                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()),
                vis_encode_type=EncoderType(
                    trainer_params.get("vis_encode_type", "simple")
                ),
            )
            self.model.create_ppo_optimizer()

        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.total_policy_loss = self.model.abs_policy_loss
        self.update_dict.update(
            {
                "value_loss": self.model.value_loss,
                "policy_loss": self.total_policy_loss,
                "update_batch": self.model.update_batch,
            }
        )

    def create_reward_signals(self, reward_signal_configs):
        """
        Create reward signals
        :param reward_signal_configs: Reward signal config.
        """
        self.reward_signals = {}
        with self.graph.as_default():
            # Create reward signals
            for reward_signal, config in reward_signal_configs.items():
                self.reward_signals[reward_signal] = create_reward_signal(
                    self, self.model, reward_signal, config
                )
                self.update_dict.update(self.reward_signals[reward_signal].update_dict)

    @timed
    def evaluate(self, brain_info):
        """
        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,
        }
        epsilon = None
        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
        if self.use_continuous_act:
            epsilon = np.random.normal(
                size=(len(brain_info.vector_observations), self.model.act_size[0])
            )
            feed_dict[self.model.epsilon] = epsilon
        feed_dict = self.fill_eval_dict(feed_dict, brain_info)
        run_out = self._execute_model(feed_dict, self.inference_dict)
        if self.use_continuous_act:
            run_out["random_normal_epsilon"] = epsilon
        return run_out

    @timed
    def update(self, mini_batch, num_sequences):
        """
        Performs update on model.
        :param mini_batch: Batch of experiences.
        :param num_sequences: Number of sequences to process.
        :return: Results of update.
        """
        feed_dict = self.construct_feed_dict(self.model, mini_batch, num_sequences)
        stats_needed = self.stats_name_to_update_name
        update_stats = {}
        # Collect feed dicts for all reward signals.
        for _, reward_signal in self.reward_signals.items():
            feed_dict.update(
                reward_signal.prepare_update(self.model, mini_batch, num_sequences)
            )
            stats_needed.update(reward_signal.stats_name_to_update_name)

        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]
        return update_stats

    def construct_feed_dict(self, model, mini_batch, num_sequences):
        feed_dict = {
            model.batch_size: num_sequences,
            model.sequence_length: self.sequence_length,
            model.mask_input: mini_batch["masks"],
            model.advantage: mini_batch["advantages"],
            model.all_old_log_probs: mini_batch["action_probs"],
        }
        for name in self.reward_signals:
            feed_dict[model.returns_holders[name]] = mini_batch[
                "{}_returns".format(name)
            ]
            feed_dict[model.old_values[name]] = mini_batch[
                "{}_value_estimates".format(name)
            ]

        if self.use_continuous_act:
            feed_dict[model.output_pre] = mini_batch["actions_pre"]
            feed_dict[model.epsilon] = mini_batch["random_normal_epsilon"]
        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"]
        if self.model.vis_obs_size > 0:
            for i, _ in enumerate(self.model.visual_in):
                feed_dict[model.visual_in[i]] = mini_batch["visual_obs%d" % i]
        if self.use_recurrent:
            mem_in = [
                mini_batch["memory"][i]
                for i in range(0, len(mini_batch["memory"]), self.sequence_length)
            ]
            feed_dict[model.memory_in] = mem_in
        return feed_dict

    def get_value_estimates(
        self, brain_info: BrainInfo, idx: int, done: bool
    ) -> Dict[str, float]:
        """
        Generates value estimates for bootstrapping.
        :param brain_info: BrainInfo to be used for bootstrapping.
        :param idx: Index in BrainInfo of agent.
        :param done: Whether or not this is the last element of the episode, in which case the value estimate will be 0.
        :return: The value estimate dictionary with key being the name of the reward signal and the value the
        corresponding value estimate.
        """

        feed_dict: Dict[tf.Tensor, Any] = {
            self.model.batch_size: 1,
            self.model.sequence_length: 1,
        }
        for i in range(len(brain_info.visual_observations)):
            feed_dict[self.model.visual_in[i]] = [
                brain_info.visual_observations[i][idx]
            ]
        if self.use_vec_obs:
            feed_dict[self.model.vector_in] = [brain_info.vector_observations[idx]]
        if self.use_recurrent:
            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[idx]]
        if not self.use_continuous_act and self.use_recurrent:
            feed_dict[self.model.prev_action] = [
                brain_info.previous_vector_actions[idx]
            ]
        value_estimates = self.sess.run(self.model.value_heads, feed_dict)

        value_estimates = {k: float(v) for k, v in value_estimates.items()}

        # If we're done, reassign all of the value estimates that need terminal states.
        if done:
            for k in value_estimates:
                if self.reward_signals[k].use_terminal_states:
                    value_estimates[k] = 0.0

        return value_estimates