ml-agents/ml-agents/mlagents/trainers/rl_trainer.py

# # Unity ML-Agents Toolkit
import logging
from typing import Dict, List, Deque, Any
import os
import tensorflow as tf
import numpy as np
from collections import deque, defaultdict

from mlagents.envs import UnityException, AllBrainInfo, ActionInfoOutputs, BrainInfo
from mlagents.trainers.buffer import Buffer
from mlagents.trainers.tf_policy import Policy
from mlagents.trainers.trainer import Trainer, UnityTrainerException
from mlagents.envs import BrainParameters

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


class RLTrainer(Trainer):
    """
    This class is the base class for trainers that use Reward Signals.
    Contains methods for adding BrainInfos to the Buffer.
    """

    def __init__(self, *args, **kwargs):
        super(RLTrainer, self).__init__(*args, **kwargs)
        self.step = 0
        # Make sure we have at least one reward_signal
        if not self.trainer_parameters["reward_signals"]:
            raise UnityTrainerException(
                "No reward signals were defined. At least one must be used with {}.".format(
                    self.__class__.__name__
                )
            )
        # collected_rewards is a dictionary from name of reward signal to a dictionary of agent_id to cumulative reward
        # used for reporting only. We always want to report the environment reward to Tensorboard, regardless
        # of what reward signals are actually present.
        self.collected_rewards = {"environment": {}}
        self.training_buffer = Buffer()
        self.episode_steps = {}

    def construct_curr_info(self, next_info: BrainInfo) -> BrainInfo:
        """
        Constructs a BrainInfo which contains the most recent previous experiences for all agents
        which correspond to the agents in a provided next_info.
        :BrainInfo next_info: A t+1 BrainInfo.
        :return: curr_info: Reconstructed BrainInfo to match agents of next_info.
        """
        visual_observations: List[List[Any]] = [
            []
        ]  # TODO add types to brain.py methods
        vector_observations = []
        text_observations = []
        memories = []
        rewards = []
        local_dones = []
        max_reacheds = []
        agents = []
        prev_vector_actions = []
        prev_text_actions = []
        action_masks = []
        for agent_id in next_info.agents:
            agent_brain_info = self.training_buffer[agent_id].last_brain_info
            if agent_brain_info is None:
                agent_brain_info = next_info
            agent_index = agent_brain_info.agents.index(agent_id)
            for i in range(len(next_info.visual_observations)):
                visual_observations[i].append(
                    agent_brain_info.visual_observations[i][agent_index]
                )
            vector_observations.append(
                agent_brain_info.vector_observations[agent_index]
            )
            text_observations.append(agent_brain_info.text_observations[agent_index])
            if self.policy.use_recurrent:
                if len(agent_brain_info.memories) > 0:
                    memories.append(agent_brain_info.memories[agent_index])
                else:
                    memories.append(self.policy.make_empty_memory(1))
            rewards.append(agent_brain_info.rewards[agent_index])
            local_dones.append(agent_brain_info.local_done[agent_index])
            max_reacheds.append(agent_brain_info.max_reached[agent_index])
            agents.append(agent_brain_info.agents[agent_index])
            prev_vector_actions.append(
                agent_brain_info.previous_vector_actions[agent_index]
            )
            prev_text_actions.append(
                agent_brain_info.previous_text_actions[agent_index]
            )
            action_masks.append(agent_brain_info.action_masks[agent_index])
        if self.policy.use_recurrent:
            memories = np.vstack(memories)
        curr_info = BrainInfo(
            visual_observations,
            vector_observations,
            text_observations,
            memories,
            rewards,
            agents,
            local_dones,
            prev_vector_actions,
            prev_text_actions,
            max_reacheds,
            action_masks,
        )
        return curr_info

    def add_experiences(
        self,
        curr_all_info: AllBrainInfo,
        next_all_info: AllBrainInfo,
        take_action_outputs: ActionInfoOutputs,
    ) -> None:
        """
        Adds experiences to each agent's experience history.
        :param curr_all_info: Dictionary of all current brains and corresponding BrainInfo.
        :param next_all_info: Dictionary of all current brains and corresponding BrainInfo.
        :param take_action_outputs: The outputs of the Policy's get_action method.
        """
        self.trainer_metrics.start_experience_collection_timer()
        if take_action_outputs:
            self.stats["Policy/Entropy"].append(take_action_outputs["entropy"].mean())
            self.stats["Policy/Learning Rate"].append(
                take_action_outputs["learning_rate"]
            )
            for name, signal in self.policy.reward_signals.items():
                self.stats[signal.value_name].append(
                    np.mean(take_action_outputs["value_heads"][name])
                )

        curr_info = curr_all_info[self.brain_name]
        next_info = next_all_info[self.brain_name]

        for agent_id in curr_info.agents:
            self.training_buffer[agent_id].last_brain_info = curr_info
            self.training_buffer[
                agent_id
            ].last_take_action_outputs = take_action_outputs

        if curr_info.agents != next_info.agents:
            curr_to_use = self.construct_curr_info(next_info)
        else:
            curr_to_use = curr_info

        tmp_rewards_dict = {}
        for name, signal in self.policy.reward_signals.items():
            tmp_rewards_dict[name] = signal.evaluate(curr_to_use, next_info)

        for agent_id in next_info.agents:
            stored_info = self.training_buffer[agent_id].last_brain_info
            stored_take_action_outputs = self.training_buffer[
                agent_id
            ].last_take_action_outputs
            if stored_info is not None:
                idx = stored_info.agents.index(agent_id)
                next_idx = next_info.agents.index(agent_id)
                if not stored_info.local_done[idx]:
                    for i, _ in enumerate(stored_info.visual_observations):
                        self.training_buffer[agent_id]["visual_obs%d" % i].append(
                            stored_info.visual_observations[i][idx]
                        )
                        self.training_buffer[agent_id]["next_visual_obs%d" % i].append(
                            next_info.visual_observations[i][next_idx]
                        )
                    if self.policy.use_vec_obs:
                        self.training_buffer[agent_id]["vector_obs"].append(
                            stored_info.vector_observations[idx]
                        )
                        self.training_buffer[agent_id]["next_vector_in"].append(
                            next_info.vector_observations[next_idx]
                        )
                    if self.policy.use_recurrent:
                        if stored_info.memories.shape[1] == 0:
                            stored_info.memories = np.zeros(
                                (len(stored_info.agents), self.policy.m_size)
                            )
                        self.training_buffer[agent_id]["memory"].append(
                            stored_info.memories[idx]
                        )

                    self.training_buffer[agent_id]["masks"].append(1.0)
                    self.training_buffer[agent_id]["done"].append(
                        next_info.local_done[next_idx]
                    )
                    # Add the outputs of the last eval
                    self.add_policy_outputs(stored_take_action_outputs, agent_id, idx)
                    # Store action masks if neccessary
                    if not self.policy.use_continuous_act:
                        self.training_buffer[agent_id]["action_mask"].append(
                            stored_info.action_masks[idx], padding_value=1
                        )
                    self.training_buffer[agent_id]["prev_action"].append(
                        stored_info.previous_vector_actions[idx]
                    )

                    values = stored_take_action_outputs["value_heads"]
                    # Add the value outputs if needed
                    self.add_rewards_outputs(
                        values, tmp_rewards_dict, agent_id, idx, next_idx
                    )

                    for name, rewards in self.collected_rewards.items():
                        if agent_id not in rewards:
                            rewards[agent_id] = 0
                        if name == "environment":
                            # Report the reward from the environment
                            rewards[agent_id] += np.array(next_info.rewards)[next_idx]
                        else:
                            # Report the reward signals
                            rewards[agent_id] += tmp_rewards_dict[name].scaled_reward[
                                next_idx
                            ]
                if not next_info.local_done[next_idx]:
                    if agent_id not in self.episode_steps:
                        self.episode_steps[agent_id] = 0
                    self.episode_steps[agent_id] += 1
        self.trainer_metrics.end_experience_collection_timer()

    def add_policy_outputs(
        self, take_action_outputs: ActionInfoOutputs, agent_id: str, agent_idx: int
    ) -> None:
        """
        Takes the output of the last action and store it into the training buffer.
        We break this out from add_experiences since it is very highly dependent
        on the type of trainer.
        :param take_action_outputs: The outputs of the Policy's get_action method.
        :param agent_id: the Agent we're adding to.
        :param agent_idx: the index of the Agent agent_id
        """
        raise UnityTrainerException(
            "The process_experiences method was not implemented."
        )

    def add_rewards_outputs(
        self,
        value: Dict[str, Any],
        rewards_dict: Dict[str, float],
        agent_id: str,
        agent_idx: int,
        agent_next_idx: int,
    ) -> None:
        """
        Takes the value and evaluated rewards output of the last action and store it
        into the training buffer. We break this out from add_experiences since it is very
        highly dependent on the type of trainer.
        :param take_action_outputs: The outputs of the Policy's get_action method.
        :param rewards_dict: Dict of rewards after evaluation
        :param agent_id: the Agent we're adding to.
        :param agent_idx: the index of the Agent agent_id in the current brain info
        :param agent_next_idx: the index of the Agent agent_id in the next brain info
        """
        raise UnityTrainerException(
            "The process_experiences method was not implemented."
        )