import sys from typing import List, Dict, Deque, TypeVar, Generic from collections import defaultdict, Counter, deque from mlagents_envs.base_env import BatchedStepResult from mlagents.trainers.trajectory import Trajectory, AgentExperience from mlagents.trainers.tf_policy import TFPolicy from mlagents.trainers.policy import Policy from mlagents.trainers.action_info import ActionInfo, ActionInfoOutputs from mlagents.trainers.stats import StatsReporter from mlagents.trainers.env_manager import get_global_agent_id T = TypeVar("T") class AgentProcessor: """ AgentProcessor contains a dictionary per-agent trajectory buffers. The buffers are indexed by agent_id. Buffer also contains an update_buffer that corresponds to the buffer used when updating the model. One AgentProcessor should be created per agent group. """ def __init__( self, policy: TFPolicy, behavior_id: str, stats_reporter: StatsReporter, max_trajectory_length: int = sys.maxsize, ): """ Create an AgentProcessor. :param trainer: Trainer instance connected to this AgentProcessor. Trainer is given trajectory when it is finished. :param policy: Policy instance associated with this AgentProcessor. :param max_trajectory_length: Maximum length of a trajectory before it is added to the trainer. :param stats_category: The category under which to write the stats. Usually, this comes from the Trainer. """ self.experience_buffers: Dict[str, List[AgentExperience]] = defaultdict(list) self.last_step_result: Dict[str, BatchedStepResult] = {} # last_take_action_outputs stores the action a_t taken before the current observation s_(t+1), while # grabbing previous_action from the policy grabs the action PRIOR to that, a_(t-1). self.last_take_action_outputs: Dict[str, ActionInfoOutputs] = {} # Note: In the future this policy reference will be the policy of the env_manager and not the trainer. # We can in that case just grab the action from the policy rather than having it passed in. self.policy = policy self.episode_steps: Counter = Counter() self.episode_rewards: Dict[str, float] = defaultdict(float) self.stats_reporter = stats_reporter self.max_trajectory_length = max_trajectory_length self.trajectory_queues: List[AgentManagerQueue[Trajectory]] = [] self.behavior_id = behavior_id def add_experiences( self, batched_step_result: BatchedStepResult, worker_id: int, previous_action: ActionInfo, ) -> None: """ Adds experiences to each agent's experience history. :param batched_step_result: current BatchedStepResult. :param previous_action: The outputs of the Policy's get_action method. """ take_action_outputs = previous_action.outputs if take_action_outputs: for _entropy in take_action_outputs["entropy"]: self.stats_reporter.add_stat("Policy/Entropy", _entropy) self.stats_reporter.add_stat( "Policy/Learning Rate", take_action_outputs["learning_rate"] ) # Make unique agent_ids that are global across workers action_global_agent_ids = [ get_global_agent_id(worker_id, ag_id) for ag_id in previous_action.agent_ids ] for global_id in action_global_agent_ids: self.last_take_action_outputs[global_id] = take_action_outputs for _id in batched_step_result.agent_id: # Assume agent_id is 1-D local_id = int( _id ) # Needed for mypy to pass since ndarray has no content type curr_agent_step = batched_step_result.get_agent_step_result(local_id) global_id = get_global_agent_id(worker_id, local_id) stored_step = self.last_step_result.get(global_id, None) stored_take_action_outputs = self.last_take_action_outputs.get( global_id, None ) if stored_step is not None and stored_take_action_outputs is not None: # We know the step is from the same worker, so use the local agent id. stored_agent_step = stored_step.get_agent_step_result(local_id) idx = stored_step.agent_id_to_index[local_id] obs = stored_agent_step.obs if not stored_agent_step.done: if self.policy.use_recurrent: memory = self.policy.retrieve_memories([global_id])[0, :] else: memory = None done = curr_agent_step.done max_step = curr_agent_step.max_step # Add the outputs of the last eval action = stored_take_action_outputs["action"][idx] if self.policy.use_continuous_act: action_pre = stored_take_action_outputs["pre_action"][idx] else: action_pre = None action_probs = stored_take_action_outputs["log_probs"][idx] action_mask = stored_agent_step.action_mask prev_action = self.policy.retrieve_previous_action([global_id])[ 0, : ] experience = AgentExperience( obs=obs, reward=curr_agent_step.reward, done=done, action=action, action_probs=action_probs, action_pre=action_pre, action_mask=action_mask, prev_action=prev_action, max_step=max_step, memory=memory, ) # Add the value outputs if needed self.experience_buffers[global_id].append(experience) self.episode_rewards[global_id] += curr_agent_step.reward if ( curr_agent_step.done or ( len(self.experience_buffers[global_id]) >= self.max_trajectory_length ) ) and len(self.experience_buffers[global_id]) > 0: # Make next AgentExperience next_obs = curr_agent_step.obs trajectory = Trajectory( steps=self.experience_buffers[global_id], agent_id=global_id, next_obs=next_obs, behavior_id=self.behavior_id, ) for traj_queue in self.trajectory_queues: traj_queue.put(trajectory) self.experience_buffers[global_id] = [] if curr_agent_step.done: self.stats_reporter.add_stat( "Environment/Cumulative Reward", self.episode_rewards.get(global_id, 0), ) self.stats_reporter.add_stat( "Environment/Episode Length", self.episode_steps.get(global_id, 0), ) del self.episode_steps[global_id] del self.episode_rewards[global_id] elif not curr_agent_step.done: self.episode_steps[global_id] += 1 self.last_step_result[global_id] = batched_step_result if "action" in take_action_outputs: self.policy.save_previous_action( previous_action.agent_ids, take_action_outputs["action"] ) def publish_trajectory_queue( self, trajectory_queue: "AgentManagerQueue[Trajectory]" ) -> None: """ Adds a trajectory queue to the list of queues to publish to when this AgentProcessor assembles a Trajectory :param trajectory_queue: Trajectory queue to publish to. """ self.trajectory_queues.append(trajectory_queue) class AgentManagerQueue(Generic[T]): """ Queue used by the AgentManager. Note that we make our own class here because in most implementations deque is sufficient and faster. However, if we want to switch to multiprocessing, we'll need to change out this implementation. """ class Empty(Exception): """ Exception for when the queue is empty. """ pass def __init__(self, behavior_id: str, maxlen: int = 1000): """ Initializes an AgentManagerQueue. Note that we can give it a behavior_id so that it can be identified separately from an AgentManager. """ self.maxlen: int = maxlen self.queue: Deque[T] = deque(maxlen=self.maxlen) self.behavior_id = behavior_id def empty(self) -> bool: return len(self.queue) == 0 def get_nowait(self) -> T: try: return self.queue.popleft() except IndexError: raise self.Empty("The AgentManagerQueue is empty.") def put(self, item: T) -> None: self.queue.append(item) class AgentManager(AgentProcessor): """ An AgentManager is an AgentProcessor that also holds a single trajectory and policy queue. Note: this leaves room for adding AgentProcessors that publish multiple trajectory queues. """ def __init__( self, policy: TFPolicy, behavior_id: str, stats_reporter: StatsReporter, max_trajectory_length: int = sys.maxsize, ): super().__init__(policy, behavior_id, stats_reporter, max_trajectory_length) self.trajectory_queue: AgentManagerQueue[Trajectory] = AgentManagerQueue( self.behavior_id ) self.policy_queue: AgentManagerQueue[Policy] = AgentManagerQueue( self.behavior_id ) self.publish_trajectory_queue(self.trajectory_queue)