# # Unity ML-Agents Toolkit # ## ML-Agent Learning (Ghost Trainer) from typing import Deque, Dict, List, cast import numpy as np from mlagents_envs.logging_util import get_logger from mlagents.trainers.brain import BrainParameters from mlagents.trainers.policy import Policy from mlagents.trainers.policy.tf_policy import TFPolicy from mlagents.trainers.trainer import Trainer from mlagents.trainers.trajectory import Trajectory from mlagents.trainers.agent_processor import AgentManagerQueue from mlagents.trainers.stats import StatsPropertyType from mlagents.trainers.behavior_id_utils import ( BehaviorIdentifiers, create_name_behavior_id, ) logger = get_logger(__name__) class GhostTrainer(Trainer): """ The GhostTrainer trains agents in adversarial games (there are teams in opposition) using a self-play mechanism. In adversarial settings with self-play, at any time, there is only a single learning team. The other team(s) is "ghosted" which means that its agents are executing fixed policies and not learning. The GhostTrainer wraps a standard RL trainer which trains the learning team and ensures that only the trajectories collected by the learning team are used for training. The GhostTrainer also maintains past policy snapshots to be used as the fixed policies when the team is not learning. The GhostTrainer is 1:1 with brain_names as the other trainers, and is responsible for one or more teams. Note, a GhostTrainer can have only one team in asymmetric games where there is only one team with a particular behavior i.e. Hide and Seek. The GhostController manages high level coordination between multiple ghost trainers. The learning team id is cycled throughout a training run. """ def __init__( self, trainer, brain_name, controller, reward_buff_cap, trainer_parameters, training, run_id, ): """ Creates a GhostTrainer. :param trainer: The trainer of the policy/policies being trained with self_play :param brain_name: The name of the brain associated with trainer config :param controller: GhostController that coordinates all ghost trainers and calculates ELO :param reward_buff_cap: Max reward history to track in the reward buffer :param trainer_parameters: The parameters for the trainer (dictionary). :param training: Whether the trainer is set for training. :param run_id: The identifier of the current run """ super(GhostTrainer, self).__init__( brain_name, trainer_parameters, training, run_id, reward_buff_cap ) self.trainer = trainer self.controller = controller self._internal_trajectory_queues: Dict[str, AgentManagerQueue[Trajectory]] = {} self._internal_policy_queues: Dict[str, AgentManagerQueue[Policy]] = {} self._team_to_name_to_policy_queue: Dict[ int, Dict[str, AgentManagerQueue[Policy]] ] = {} self._name_to_parsed_behavior_id: Dict[str, BehaviorIdentifiers] = {} # assign ghost's stats collection to wrapped trainer's self._stats_reporter = self.trainer.stats_reporter # Set the logging to print ELO in the console self._stats_reporter.add_property(StatsPropertyType.SELF_PLAY, True) self_play_parameters = trainer_parameters["self_play"] self.window = self_play_parameters.get("window", 10) self.play_against_latest_model_ratio = self_play_parameters.get( "play_against_latest_model_ratio", 0.5 ) if ( self.play_against_latest_model_ratio > 1.0 or self.play_against_latest_model_ratio < 0.0 ): logger.warning( "The play_against_latest_model_ratio is not between 0 and 1." ) self.steps_between_save = self_play_parameters.get("save_steps", 20000) self.steps_between_swap = self_play_parameters.get("swap_steps", 20000) self.steps_to_train_team = self_play_parameters.get("team_change", 100000) if self.steps_to_train_team > self.get_max_steps: logger.warning( "The max steps of the GhostTrainer for behavior name {} is less than team change. This team will not face \ opposition that has been trained if the opposition is managed by a different GhostTrainer as in an \ asymmetric game.".format( self.brain_name ) ) # Counts the The number of steps of the ghost policies. Snapshot swapping # depends on this counter whereas snapshot saving and team switching depends # on the wrapped. This ensures that all teams train for the same number of trainer # steps. self.ghost_step: int = 0 # A list of dicts from brain name to a single snapshot for this trainer's policies self.policy_snapshots: List[Dict[str, List[float]]] = [] # A dict from brain name to the current snapshot of this trainer's policies self.current_policy_snapshot: Dict[str, List[float]] = {} self.snapshot_counter: int = 0 self.policies: Dict[str, TFPolicy] = {} # wrapped_training_team and learning team need to be separate # in the situation where new agents are created destroyed # after learning team switches. These agents need to be added # to trainers properly. self._learning_team: int = None self.wrapped_trainer_team: int = None self.last_save: int = 0 self.last_swap: int = 0 self.last_team_change: int = 0 # Chosen because it is the initial ELO in Chess self.initial_elo: float = self_play_parameters.get("initial_elo", 1200.0) self.policy_elos: List[float] = [self.initial_elo] * ( self.window + 1 ) # for learning policy self.current_opponent: int = 0 @property def get_step(self) -> int: """ Returns the number of steps the wrapped trainer has performed :return: the step count of the wrapped trainer """ return self.trainer.get_step @property def reward_buffer(self) -> Deque[float]: """ Returns the reward buffer. The reward buffer contains the cumulative rewards of the most recent episodes completed by agents using this trainer. :return: the reward buffer. """ return self.trainer.reward_buffer @property def current_elo(self) -> float: """ Gets ELO of current policy which is always last in the list :return: ELO of current policy """ return self.policy_elos[-1] def change_current_elo(self, change: float) -> None: """ Changes elo of current policy which is always last in the list :param change: Amount to change current elo by """ self.policy_elos[-1] += change def get_opponent_elo(self) -> float: """ Get elo of current opponent policy :return: ELO of current opponent policy """ return self.policy_elos[self.current_opponent] def change_opponent_elo(self, change: float) -> None: """ Changes elo of current opponent policy :param change: Amount to change current opponent elo by """ self.policy_elos[self.current_opponent] -= change def _process_trajectory(self, trajectory: Trajectory) -> None: """ Determines the final result of an episode and asks the GhostController to calculate the ELO change. The GhostController changes the ELO of the opponent policy since this may be in a different GhostTrainer i.e. in asymmetric games. We assume the last reward determines the winner. :param trajectory: Trajectory. """ if trajectory.done_reached: # Assumption is that final reward is >0/0/<0 for win/draw/loss final_reward = trajectory.steps[-1].reward result = 0.5 if final_reward > 0: result = 1.0 elif final_reward < 0: result = 0.0 change = self.controller.compute_elo_rating_changes( self.current_elo, result ) self.change_current_elo(change) self._stats_reporter.add_stat("Self-play/ELO", self.current_elo) def advance(self) -> None: """ Steps the trainer, passing trajectories to wrapped trainer and calling trainer advance """ for trajectory_queue in self.trajectory_queues: parsed_behavior_id = self._name_to_parsed_behavior_id[ trajectory_queue.behavior_id ] if parsed_behavior_id.team_id == self._learning_team: # With a future multiagent trainer, this will be indexed by 'role' internal_trajectory_queue = self._internal_trajectory_queues[ parsed_behavior_id.brain_name ] try: # We grab at most the maximum length of the queue. # This ensures that even if the queue is being filled faster than it is # being emptied, the trajectories in the queue are on-policy. for _ in range(trajectory_queue.maxlen): t = trajectory_queue.get_nowait() # adds to wrapped trainers queue internal_trajectory_queue.put(t) self._process_trajectory(t) except AgentManagerQueue.Empty: pass else: # Dump trajectories from non-learning policy try: for _ in range(trajectory_queue.maxlen): t = trajectory_queue.get_nowait() # count ghost steps self.ghost_step += len(t.steps) except AgentManagerQueue.Empty: pass self.next_summary_step = self.trainer.next_summary_step self.trainer.advance() if self.get_step - self.last_team_change > self.steps_to_train_team: self.controller.change_training_team(self.get_step) self.last_team_change = self.get_step next_learning_team = self.controller.get_learning_team # CASE 1: Current learning team is managed by this GhostTrainer. # If the learning team changes, the following loop over queues will push the # new policy into the policy queue for the new learning agent if # that policy is managed by this GhostTrainer. Otherwise, it will save the current snapshot. # CASE 2: Current learning team is managed by a different GhostTrainer. # If the learning team changes to a team managed by this GhostTrainer, this loop # will push the current_snapshot into the correct queue. Otherwise, # it will continue skipping and swap_snapshot will continue to handle # pushing fixed snapshots # Case 3: No team change. The if statement just continues to push the policy # into the correct queue (or not if not learning team). for brain_name in self._internal_policy_queues: internal_policy_queue = self._internal_policy_queues[brain_name] try: policy = cast(TFPolicy, internal_policy_queue.get_nowait()) self.current_policy_snapshot[brain_name] = policy.get_weights() except AgentManagerQueue.Empty: pass if next_learning_team in self._team_to_name_to_policy_queue: name_to_policy_queue = self._team_to_name_to_policy_queue[ next_learning_team ] if brain_name in name_to_policy_queue: behavior_id = create_name_behavior_id( brain_name, next_learning_team ) policy = self.get_policy(behavior_id) policy.load_weights(self.current_policy_snapshot[brain_name]) name_to_policy_queue[brain_name].put(policy) # Note save and swap should be on different step counters. # We don't want to save unless the policy is learning. if self.get_step - self.last_save > self.steps_between_save: self._save_snapshot() self.last_save = self.get_step if ( self._learning_team != next_learning_team or self.ghost_step - self.last_swap > self.steps_between_swap ): self._learning_team = next_learning_team self._swap_snapshots() self.last_swap = self.ghost_step def end_episode(self): """ Forwarding call to wrapped trainers end_episode """ self.trainer.end_episode() def save_model(self, name_behavior_id: str) -> None: """ Forwarding call to wrapped trainers save_model """ parsed_behavior_id = self._name_to_parsed_behavior_id[name_behavior_id] brain_name = parsed_behavior_id.brain_name self.trainer.save_model(brain_name) def export_model(self, name_behavior_id: str) -> None: """ Forwarding call to wrapped trainers export_model. """ parsed_behavior_id = self._name_to_parsed_behavior_id[name_behavior_id] brain_name = parsed_behavior_id.brain_name self.trainer.export_model(brain_name) def create_policy( self, parsed_behavior_id: BehaviorIdentifiers, brain_parameters: BrainParameters ) -> TFPolicy: """ Creates policy with the wrapped trainer's create_policy function The first policy encountered sets the wrapped trainer team. This is to ensure that all agents from the same multi-agent team are grouped. All policies associated with this team are added to the wrapped trainer to be trained. """ policy = self.trainer.create_policy(parsed_behavior_id, brain_parameters) policy.create_tf_graph() policy.init_load_weights() team_id = parsed_behavior_id.team_id self.controller.subscribe_team_id(team_id, self) # First policy or a new agent on the same team encountered if self.wrapped_trainer_team is None or team_id == self.wrapped_trainer_team: internal_trainer_policy = self.trainer.create_policy( parsed_behavior_id, brain_parameters ) internal_trainer_policy.create_tf_graph() internal_trainer_policy.init_load_weights() self.current_policy_snapshot[ parsed_behavior_id.brain_name ] = internal_trainer_policy.get_weights() policy.load_weights(internal_trainer_policy.get_weights()) self._save_snapshot() # Need to save after trainer initializes policy self.trainer.add_policy(parsed_behavior_id, internal_trainer_policy) self._learning_team = self.controller.get_learning_team self.wrapped_trainer_team = team_id return policy def add_policy( self, parsed_behavior_id: BehaviorIdentifiers, policy: TFPolicy ) -> None: """ Adds policy to GhostTrainer. :param parsed_behavior_id: Behavior ID that the policy should belong to. :param policy: Policy to associate with name_behavior_id. """ name_behavior_id = parsed_behavior_id.behavior_id self._name_to_parsed_behavior_id[name_behavior_id] = parsed_behavior_id self.policies[name_behavior_id] = policy def get_policy(self, name_behavior_id: str) -> TFPolicy: """ Gets policy associated with name_behavior_id :param name_behavior_id: Fully qualified behavior name :return: Policy associated with name_behavior_id """ return self.policies[name_behavior_id] def _save_snapshot(self) -> None: """ Saves a snapshot of the current weights of the policy and maintains the policy_snapshots according to the window size """ for brain_name in self.current_policy_snapshot: current_snapshot_for_brain_name = self.current_policy_snapshot[brain_name] try: self.policy_snapshots[self.snapshot_counter][ brain_name ] = current_snapshot_for_brain_name except IndexError: self.policy_snapshots.append( {brain_name: current_snapshot_for_brain_name} ) self.policy_elos[self.snapshot_counter] = self.current_elo self.snapshot_counter = (self.snapshot_counter + 1) % self.window def _swap_snapshots(self) -> None: """ Swaps the appropriate weight to the policy and pushes it to respective policy queues """ for team_id in self._team_to_name_to_policy_queue: if team_id == self._learning_team: continue elif np.random.uniform() < (1 - self.play_against_latest_model_ratio): x = np.random.randint(len(self.policy_snapshots)) snapshot = self.policy_snapshots[x] else: snapshot = self.current_policy_snapshot x = "current" self.current_opponent = -1 if x == "current" else x name_to_policy_queue = self._team_to_name_to_policy_queue[team_id] for brain_name in self._team_to_name_to_policy_queue[team_id]: behavior_id = create_name_behavior_id(brain_name, team_id) policy = self.get_policy(behavior_id) policy.load_weights(snapshot[brain_name]) name_to_policy_queue[brain_name].put(policy) logger.debug( "Step {}: Swapping snapshot {} to id {} with team {} learning".format( self.ghost_step, x, behavior_id, self._learning_team ) ) def publish_policy_queue(self, policy_queue: AgentManagerQueue[Policy]) -> None: """ Adds a policy queue for every member of the team to the list of queues to publish to when this Trainer makes a policy update. Creates an internal policy queue for the wrapped trainer to push to. The GhostTrainer pushes all policies to the env. :param queue: Policy queue to publish to. """ super().publish_policy_queue(policy_queue) parsed_behavior_id = self._name_to_parsed_behavior_id[policy_queue.behavior_id] try: self._team_to_name_to_policy_queue[parsed_behavior_id.team_id][ parsed_behavior_id.brain_name ] = policy_queue except KeyError: self._team_to_name_to_policy_queue[parsed_behavior_id.team_id] = { parsed_behavior_id.brain_name: policy_queue } if parsed_behavior_id.team_id == self.wrapped_trainer_team: # With a future multiagent trainer, this will be indexed by 'role' internal_policy_queue: AgentManagerQueue[Policy] = AgentManagerQueue( parsed_behavior_id.brain_name ) self._internal_policy_queues[ parsed_behavior_id.brain_name ] = internal_policy_queue self.trainer.publish_policy_queue(internal_policy_queue) def subscribe_trajectory_queue( self, trajectory_queue: AgentManagerQueue[Trajectory] ) -> None: """ Adds a trajectory queue for every member of the team to the list of queues for the trainer to ingest Trajectories from. Creates an internal trajectory queue to push trajectories from the learning team. The wrapped trainer subscribes to this queue. :param queue: Trajectory queue to publish to. """ super().subscribe_trajectory_queue(trajectory_queue) parsed_behavior_id = self._name_to_parsed_behavior_id[ trajectory_queue.behavior_id ] if parsed_behavior_id.team_id == self.wrapped_trainer_team: # With a future multiagent trainer, this will be indexed by 'role' internal_trajectory_queue: AgentManagerQueue[ Trajectory ] = AgentManagerQueue(parsed_behavior_id.brain_name) self._internal_trajectory_queues[ parsed_behavior_id.brain_name ] = internal_trajectory_queue self.trainer.subscribe_trajectory_queue(internal_trajectory_queue)