Source code for ray.rllib.evaluation.sampler

from abc import abstractmethod, ABCMeta
from collections import defaultdict, namedtuple
import logging
import numpy as np
import queue
import threading
import time
import tree  # pip install dm_tree
from typing import Any, Callable, Dict, List, Iterator, Optional, Set, Tuple,\
    Type, TYPE_CHECKING, Union

from ray.util.debug import log_once
from ray.rllib.evaluation.collectors.sample_collector import \
    SampleCollector
from ray.rllib.evaluation.collectors.simple_list_collector import \
    SimpleListCollector
from ray.rllib.evaluation.episode import MultiAgentEpisode
from ray.rllib.evaluation.rollout_metrics import RolloutMetrics
from ray.rllib.evaluation.sample_batch_builder import \
    MultiAgentSampleBatchBuilder
from ray.rllib.env.base_env import BaseEnv, ASYNC_RESET_RETURN
from ray.rllib.env.wrappers.atari_wrappers import get_wrapper_by_cls, \
    MonitorEnv
from ray.rllib.models.preprocessors import Preprocessor
from ray.rllib.offline import InputReader
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.policy_map import PolicyMap
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.debug import summarize
from ray.rllib.utils.deprecation import deprecation_warning
from ray.rllib.utils.filter import Filter
from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.spaces.space_utils import clip_action, \
    unsquash_action, unbatch
from ray.rllib.utils.typing import SampleBatchType, AgentID, PolicyID, \
    EnvObsType, EnvInfoDict, EnvID, MultiEnvDict, EnvActionType, \
    TensorStructType

if TYPE_CHECKING:
    from ray.rllib.agents.callbacks import DefaultCallbacks
    from ray.rllib.evaluation.observation_function import ObservationFunction
    from ray.rllib.evaluation.rollout_worker import RolloutWorker
    from ray.rllib.utils import try_import_tf
    _, tf, _ = try_import_tf()
    from gym.envs.classic_control.rendering import SimpleImageViewer

logger = logging.getLogger(__name__)

PolicyEvalData = namedtuple("PolicyEvalData", [
    "env_id", "agent_id", "obs", "info", "rnn_state", "prev_action",
    "prev_reward"
])

# A batch of RNN states with dimensions [state_index, batch, state_object].
StateBatch = List[List[Any]]


class NewEpisodeDefaultDict(defaultdict):
    def __missing__(self, env_id):
        if self.default_factory is None:
            raise KeyError(env_id)
        else:
            ret = self[env_id] = self.default_factory(env_id)
            return ret


class _PerfStats:
    """Sampler perf stats that will be included in rollout metrics."""

    def __init__(self):
        self.iters = 0
        self.raw_obs_processing_time = 0.0
        self.inference_time = 0.0
        self.action_processing_time = 0.0
        self.env_wait_time = 0.0
        self.env_render_time = 0.0

    def get(self):
        # Mean multiplicator (1000 = ms -> sec).
        factor = 1000 / self.iters
        return {
            # Raw observation preprocessing.
            "mean_raw_obs_processing_ms": self.raw_obs_processing_time *
            factor,
            # Computing actions through policy.
            "mean_inference_ms": self.inference_time * factor,
            # Processing actions (to be sent to env, e.g. clipping).
            "mean_action_processing_ms": self.action_processing_time * factor,
            # Waiting for environment (during poll).
            "mean_env_wait_ms": self.env_wait_time * factor,
            # Environment rendering (False by default).
            "mean_env_render_ms": self.env_render_time * factor,
        }


@DeveloperAPI
class SamplerInput(InputReader, metaclass=ABCMeta):
    """Reads input experiences from an existing sampler."""

    @override(InputReader)
    def next(self) -> SampleBatchType:
        batches = [self.get_data()]
        batches.extend(self.get_extra_batches())
        if len(batches) > 1:
            return batches[0].concat_samples(batches)
        else:
            return batches[0]

    @abstractmethod
    @DeveloperAPI
    def get_data(self) -> SampleBatchType:
        raise NotImplementedError

    @abstractmethod
    @DeveloperAPI
    def get_metrics(self) -> List[RolloutMetrics]:
        raise NotImplementedError

    @abstractmethod
    @DeveloperAPI
    def get_extra_batches(self) -> List[SampleBatchType]:
        raise NotImplementedError


[docs]@DeveloperAPI class SyncSampler(SamplerInput): """Sync SamplerInput that collects experiences when `get_data()` is called. """ def __init__( self, *, worker: "RolloutWorker", env: BaseEnv, clip_rewards: Union[bool, float], rollout_fragment_length: int, count_steps_by: str = "env_steps", callbacks: "DefaultCallbacks", horizon: int = None, multiple_episodes_in_batch: bool = False, normalize_actions: bool = True, clip_actions: bool = False, soft_horizon: bool = False, no_done_at_end: bool = False, observation_fn: "ObservationFunction" = None, sample_collector_class: Optional[Type[SampleCollector]] = None, render: bool = False, # Obsolete. policies=None, policy_mapping_fn=None, preprocessors=None, obs_filters=None, tf_sess=None, ): """Initializes a SyncSampler object. Args: worker (RolloutWorker): The RolloutWorker that will use this Sampler for sampling. env (Env): Any Env object. Will be converted into an RLlib BaseEnv. clip_rewards (Union[bool, float]): True for +/-1.0 clipping, actual float value for +/- value clipping. False for no clipping. rollout_fragment_length (int): The length of a fragment to collect before building a SampleBatch from the data and resetting the SampleBatchBuilder object. callbacks (Callbacks): The Callbacks object to use when episode events happen during rollout. horizon (Optional[int]): Hard-reset the Env multiple_episodes_in_batch (bool): Whether to pack multiple episodes into each batch. This guarantees batches will be exactly `rollout_fragment_length` in size. normalize_actions (bool): Whether to normalize actions to the action space's bounds. clip_actions (bool): Whether to clip actions according to the given action_space's bounds. soft_horizon (bool): If True, calculate bootstrapped values as if episode had ended, but don't physically reset the environment when the horizon is hit. no_done_at_end (bool): Ignore the done=True at the end of the episode and instead record done=False. observation_fn (Optional[ObservationFunction]): Optional multi-agent observation func to use for preprocessing observations. sample_collector_class (Optional[Type[SampleCollector]]): An optional Samplecollector sub-class to use to collect, store, and retrieve environment-, model-, and sampler data. render (bool): Whether to try to render the environment after each step. """ # All of the following arguments are deprecated. They will instead be # provided via the passed in `worker` arg, e.g. `worker.policy_map`. if log_once("deprecated_sync_sampler_args"): if policies is not None: deprecation_warning(old="policies") if policy_mapping_fn is not None: deprecation_warning(old="policy_mapping_fn") if preprocessors is not None: deprecation_warning(old="preprocessors") if obs_filters is not None: deprecation_warning(old="obs_filters") if tf_sess is not None: deprecation_warning(old="tf_sess") self.base_env = BaseEnv.to_base_env(env) self.rollout_fragment_length = rollout_fragment_length self.horizon = horizon self.extra_batches = queue.Queue() self.perf_stats = _PerfStats() if not sample_collector_class: sample_collector_class = SimpleListCollector self.sample_collector = sample_collector_class( worker.policy_map, clip_rewards, callbacks, multiple_episodes_in_batch, rollout_fragment_length, count_steps_by=count_steps_by) self.render = render # Create the rollout generator to use for calls to `get_data()`. self._env_runner = _env_runner( worker, self.base_env, self.extra_batches.put, self.horizon, normalize_actions, clip_actions, multiple_episodes_in_batch, callbacks, self.perf_stats, soft_horizon, no_done_at_end, observation_fn, self.sample_collector, self.render) self.metrics_queue = queue.Queue() @override(SamplerInput) def get_data(self) -> SampleBatchType: while True: item = next(self._env_runner) if isinstance(item, RolloutMetrics): self.metrics_queue.put(item) else: return item @override(SamplerInput) def get_metrics(self) -> List[RolloutMetrics]: completed = [] while True: try: completed.append(self.metrics_queue.get_nowait()._replace( perf_stats=self.perf_stats.get())) except queue.Empty: break return completed @override(SamplerInput) def get_extra_batches(self) -> List[SampleBatchType]: extra = [] while True: try: extra.append(self.extra_batches.get_nowait()) except queue.Empty: break return extra
[docs]@DeveloperAPI class AsyncSampler(threading.Thread, SamplerInput): """Async SamplerInput that collects experiences in thread and queues them. Once started, experiences are continuously collected and put into a Queue, from where they can be unqueued by the caller of `get_data()`. """ def __init__( self, *, worker: "RolloutWorker", env: BaseEnv, clip_rewards: Union[bool, float], rollout_fragment_length: int, count_steps_by: str = "env_steps", callbacks: "DefaultCallbacks", horizon: int = None, multiple_episodes_in_batch: bool = False, normalize_actions: bool = True, clip_actions: bool = False, blackhole_outputs: bool = False, soft_horizon: bool = False, no_done_at_end: bool = False, observation_fn: "ObservationFunction" = None, sample_collector_class: Optional[Type[SampleCollector]] = None, render: bool = False, # Obsolete. policies=None, policy_mapping_fn=None, preprocessors=None, obs_filters=None, tf_sess=None, ): """Initializes a AsyncSampler object. Args: worker (RolloutWorker): The RolloutWorker that will use this Sampler for sampling. env (Env): Any Env object. Will be converted into an RLlib BaseEnv. clip_rewards (Union[bool, float]): True for +/-1.0 clipping, actual float value for +/- value clipping. False for no clipping. rollout_fragment_length (int): The length of a fragment to collect before building a SampleBatch from the data and resetting the SampleBatchBuilder object. count_steps_by (str): Either "env_steps" or "agent_steps". Refers to the unit of `rollout_fragment_length`. callbacks (Callbacks): The Callbacks object to use when episode events happen during rollout. horizon (Optional[int]): Hard-reset the Env multiple_episodes_in_batch (bool): Whether to pack multiple episodes into each batch. This guarantees batches will be exactly `rollout_fragment_length` in size. normalize_actions (bool): Whether to normalize actions to the action space's bounds. clip_actions (bool): Whether to clip actions according to the given action_space's bounds. blackhole_outputs (bool): Whether to collect samples, but then not further process or store them (throw away all samples). soft_horizon (bool): If True, calculate bootstrapped values as if episode had ended, but don't physically reset the environment when the horizon is hit. no_done_at_end (bool): Ignore the done=True at the end of the episode and instead record done=False. observation_fn (Optional[ObservationFunction]): Optional multi-agent observation func to use for preprocessing observations. sample_collector_class (Optional[Type[SampleCollector]]): An optional Samplecollector sub-class to use to collect, store, and retrieve environment-, model-, and sampler data. render (bool): Whether to try to render the environment after each step. """ # All of the following arguments are deprecated. They will instead be # provided via the passed in `worker` arg, e.g. `worker.policy_map`. if log_once("deprecated_async_sampler_args"): if policies is not None: deprecation_warning(old="policies") if policy_mapping_fn is not None: deprecation_warning(old="policy_mapping_fn") if preprocessors is not None: deprecation_warning(old="preprocessors") if obs_filters is not None: deprecation_warning(old="obs_filters") if tf_sess is not None: deprecation_warning(old="tf_sess") self.worker = worker for _, f in worker.filters.items(): assert getattr(f, "is_concurrent", False), \ "Observation Filter must support concurrent updates." self.base_env = BaseEnv.to_base_env(env) threading.Thread.__init__(self) self.queue = queue.Queue(5) self.extra_batches = queue.Queue() self.metrics_queue = queue.Queue() self.rollout_fragment_length = rollout_fragment_length self.horizon = horizon self.clip_rewards = clip_rewards self.daemon = True self.multiple_episodes_in_batch = multiple_episodes_in_batch self.callbacks = callbacks self.normalize_actions = normalize_actions self.clip_actions = clip_actions self.blackhole_outputs = blackhole_outputs self.soft_horizon = soft_horizon self.no_done_at_end = no_done_at_end self.perf_stats = _PerfStats() self.shutdown = False self.observation_fn = observation_fn self.render = render if not sample_collector_class: sample_collector_class = SimpleListCollector self.sample_collector = sample_collector_class( self.worker.policy_map, self.clip_rewards, self.callbacks, self.multiple_episodes_in_batch, self.rollout_fragment_length, count_steps_by=count_steps_by)
[docs] @override(threading.Thread) def run(self): try: self._run() except BaseException as e: self.queue.put(e) raise e
def _run(self): if self.blackhole_outputs: queue_putter = (lambda x: None) extra_batches_putter = (lambda x: None) else: queue_putter = self.queue.put extra_batches_putter = ( lambda x: self.extra_batches.put(x, timeout=600.0)) env_runner = _env_runner( self.worker, self.base_env, extra_batches_putter, self.horizon, self.normalize_actions, self.clip_actions, self.multiple_episodes_in_batch, self.callbacks, self.perf_stats, self.soft_horizon, self.no_done_at_end, self.observation_fn, self.sample_collector, self.render) while not self.shutdown: # The timeout variable exists because apparently, if one worker # dies, the other workers won't die with it, unless the timeout is # set to some large number. This is an empirical observation. item = next(env_runner) if isinstance(item, RolloutMetrics): self.metrics_queue.put(item) else: queue_putter(item) @override(SamplerInput) def get_data(self) -> SampleBatchType: if not self.is_alive(): raise RuntimeError("Sampling thread has died") rollout = self.queue.get(timeout=600.0) # Propagate errors. if isinstance(rollout, BaseException): raise rollout return rollout @override(SamplerInput) def get_metrics(self) -> List[RolloutMetrics]: completed = [] while True: try: completed.append(self.metrics_queue.get_nowait()._replace( perf_stats=self.perf_stats.get())) except queue.Empty: break return completed @override(SamplerInput) def get_extra_batches(self) -> List[SampleBatchType]: extra = [] while True: try: extra.append(self.extra_batches.get_nowait()) except queue.Empty: break return extra
def _env_runner( worker: "RolloutWorker", base_env: BaseEnv, extra_batch_callback: Callable[[SampleBatchType], None], horizon: int, normalize_actions: bool, clip_actions: bool, multiple_episodes_in_batch: bool, callbacks: "DefaultCallbacks", perf_stats: _PerfStats, soft_horizon: bool, no_done_at_end: bool, observation_fn: "ObservationFunction", sample_collector: Optional[SampleCollector] = None, render: bool = None, ) -> Iterator[SampleBatchType]: """This implements the common experience collection logic. Args: worker (RolloutWorker): Reference to the current rollout worker. base_env (BaseEnv): Env implementing BaseEnv. extra_batch_callback (fn): function to send extra batch data to. horizon (int): Horizon of the episode. multiple_episodes_in_batch (bool): Whether to pack multiple episodes into each batch. This guarantees batches will be exactly `rollout_fragment_length` in size. normalize_actions (bool): Whether to normalize actions to the action space's bounds. clip_actions (bool): Whether to clip actions to the space range. callbacks (DefaultCallbacks): User callbacks to run on episode events. perf_stats (_PerfStats): Record perf stats into this object. soft_horizon (bool): Calculate rewards but don't reset the environment when the horizon is hit. no_done_at_end (bool): Ignore the done=True at the end of the episode and instead record done=False. observation_fn (ObservationFunction): Optional multi-agent observation func to use for preprocessing observations. sample_collector (Optional[SampleCollector]): An optional SampleCollector object to use. render (bool): Whether to try to render the environment after each step. Yields: rollout (SampleBatch): Object containing state, action, reward, terminal condition, and other fields as dictated by `policy`. """ # May be populated with used for image rendering simple_image_viewer: Optional["SimpleImageViewer"] = None # Try to get Env's `max_episode_steps` prop. If it doesn't exist, ignore # error and continue with max_episode_steps=None. max_episode_steps = None try: max_episode_steps = base_env.get_unwrapped()[0].spec.max_episode_steps except Exception: pass # Trainer has a given `horizon` setting. if horizon: # `horizon` is larger than env's limit. if max_episode_steps and horizon > max_episode_steps: # Try to override the env's own max-step setting with our horizon. # If this won't work, throw an error. try: base_env.get_unwrapped()[0].spec.max_episode_steps = horizon base_env.get_unwrapped()[0]._max_episode_steps = horizon except Exception: raise ValueError( "Your `horizon` setting ({}) is larger than the Env's own " "timestep limit ({}), which seems to be unsettable! Try " "to increase the Env's built-in limit to be at least as " "large as your wanted `horizon`.".format( horizon, max_episode_steps)) # Otherwise, set Trainer's horizon to env's max-steps. elif max_episode_steps: horizon = max_episode_steps logger.debug( "No episode horizon specified, setting it to Env's limit ({}).". format(max_episode_steps)) # No horizon/max_episode_steps -> Episodes may be infinitely long. else: horizon = float("inf") logger.debug("No episode horizon specified, assuming inf.") # Pool of batch builders, which can be shared across episodes to pack # trajectory data. batch_builder_pool: List[MultiAgentSampleBatchBuilder] = [] def get_batch_builder(): if batch_builder_pool: return batch_builder_pool.pop() else: return None def new_episode(env_id): episode = MultiAgentEpisode( worker.policy_map, worker.policy_mapping_fn, get_batch_builder, extra_batch_callback, env_id=env_id, worker=worker, ) # Call each policy's Exploration.on_episode_start method. # Note: This may break the exploration (e.g. ParameterNoise) of # policies in the `policy_map` that have not been recently used # (and are therefore stashed to disk). However, we certainly do not # want to loop through all (even stashed) policies here as that # would counter the purpose of the LRU policy caching. for p in worker.policy_map.cache.values(): if getattr(p, "exploration", None) is not None: p.exploration.on_episode_start( policy=p, environment=base_env, episode=episode, tf_sess=p.get_session()) callbacks.on_episode_start( worker=worker, base_env=base_env, policies=worker.policy_map, episode=episode, env_index=env_id, ) return episode active_episodes: Dict[EnvID, MultiAgentEpisode] = \ NewEpisodeDefaultDict(new_episode) while True: perf_stats.iters += 1 t0 = time.time() # Get observations from all ready agents. # types: MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, ... unfiltered_obs, rewards, dones, infos, off_policy_actions = \ base_env.poll() perf_stats.env_wait_time += time.time() - t0 if log_once("env_returns"): logger.info("Raw obs from env: {}".format( summarize(unfiltered_obs))) logger.info("Info return from env: {}".format(summarize(infos))) # Process observations and prepare for policy evaluation. t1 = time.time() # types: Set[EnvID], Dict[PolicyID, List[PolicyEvalData]], # List[Union[RolloutMetrics, SampleBatchType]] active_envs, to_eval, outputs = \ _process_observations( worker=worker, base_env=base_env, active_episodes=active_episodes, unfiltered_obs=unfiltered_obs, rewards=rewards, dones=dones, infos=infos, horizon=horizon, multiple_episodes_in_batch=multiple_episodes_in_batch, callbacks=callbacks, soft_horizon=soft_horizon, no_done_at_end=no_done_at_end, observation_fn=observation_fn, sample_collector=sample_collector, ) perf_stats.raw_obs_processing_time += time.time() - t1 for o in outputs: yield o # Do batched policy eval (accross vectorized envs). t2 = time.time() # types: Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]] eval_results = _do_policy_eval( to_eval=to_eval, policies=worker.policy_map, sample_collector=sample_collector, active_episodes=active_episodes, ) perf_stats.inference_time += time.time() - t2 # Process results and update episode state. t3 = time.time() actions_to_send: Dict[EnvID, Dict[AgentID, EnvActionType]] = \ _process_policy_eval_results( to_eval=to_eval, eval_results=eval_results, active_episodes=active_episodes, active_envs=active_envs, off_policy_actions=off_policy_actions, policies=worker.policy_map, normalize_actions=normalize_actions, clip_actions=clip_actions, ) perf_stats.action_processing_time += time.time() - t3 # Return computed actions to ready envs. We also send to envs that have # taken off-policy actions; those envs are free to ignore the action. t4 = time.time() base_env.send_actions(actions_to_send) perf_stats.env_wait_time += time.time() - t4 # Try to render the env, if required. if render: t5 = time.time() # Render can either return an RGB image (uint8 [w x h x 3] numpy # array) or take care of rendering itself (returning True). rendered = base_env.try_render() # Rendering returned an image -> Display it in a SimpleImageViewer. if isinstance(rendered, np.ndarray) and len(rendered.shape) == 3: # ImageViewer not defined yet, try to create one. if simple_image_viewer is None: try: from gym.envs.classic_control.rendering import \ SimpleImageViewer simple_image_viewer = SimpleImageViewer() except (ImportError, ModuleNotFoundError): render = False # disable rendering logger.warning( "Could not import gym.envs.classic_control." "rendering! Try `pip install gym[all]`.") if simple_image_viewer: simple_image_viewer.imshow(rendered) elif rendered not in [True, False, None]: raise ValueError( "The env's ({base_env}) `try_render()` method returned an" " unsupported value! Make sure you either return a " "uint8/w x h x 3 (RGB) image or handle rendering in a " "window and then return `True`.") perf_stats.env_render_time += time.time() - t5 def _process_observations( *, worker: "RolloutWorker", base_env: BaseEnv, active_episodes: Dict[EnvID, MultiAgentEpisode], unfiltered_obs: Dict[EnvID, Dict[AgentID, EnvObsType]], rewards: Dict[EnvID, Dict[AgentID, float]], dones: Dict[EnvID, Dict[AgentID, bool]], infos: Dict[EnvID, Dict[AgentID, EnvInfoDict]], horizon: int, multiple_episodes_in_batch: bool, callbacks: "DefaultCallbacks", soft_horizon: bool, no_done_at_end: bool, observation_fn: "ObservationFunction", sample_collector: SampleCollector, ) -> Tuple[Set[EnvID], Dict[PolicyID, List[PolicyEvalData]], List[Union[ RolloutMetrics, SampleBatchType]]]: """Record new data from the environment and prepare for policy evaluation. Args: worker (RolloutWorker): Reference to the current rollout worker. base_env (BaseEnv): Env implementing BaseEnv. active_episodes (Dict[EnvID, MultiAgentEpisode]): Mapping from episode ID to currently ongoing MultiAgentEpisode object. unfiltered_obs (dict): Doubly keyed dict of env-ids -> agent ids -> unfiltered observation tensor, returned by a `BaseEnv.poll()` call. rewards (dict): Doubly keyed dict of env-ids -> agent ids -> rewards tensor, returned by a `BaseEnv.poll()` call. dones (dict): Doubly keyed dict of env-ids -> agent ids -> boolean done flags, returned by a `BaseEnv.poll()` call. infos (dict): Doubly keyed dict of env-ids -> agent ids -> info dicts, returned by a `BaseEnv.poll()` call. horizon (int): Horizon of the episode. multiple_episodes_in_batch (bool): Whether to pack multiple episodes into each batch. This guarantees batches will be exactly `rollout_fragment_length` in size. callbacks (DefaultCallbacks): User callbacks to run on episode events. soft_horizon (bool): Calculate rewards but don't reset the environment when the horizon is hit. no_done_at_end (bool): Ignore the done=True at the end of the episode and instead record done=False. observation_fn (ObservationFunction): Optional multi-agent observation func to use for preprocessing observations. sample_collector (SampleCollector): The SampleCollector object used to store and retrieve environment samples. Returns: Tuple: - active_envs: Set of non-terminated env ids. - to_eval: Map of policy_id to list of agent PolicyEvalData. - outputs: List of metrics and samples to return from the sampler. """ # Output objects. active_envs: Set[EnvID] = set() to_eval: Dict[PolicyID, List[PolicyEvalData]] = defaultdict(list) outputs: List[Union[RolloutMetrics, SampleBatchType]] = [] # For each (vectorized) sub-environment. # types: EnvID, Dict[AgentID, EnvObsType] for env_id, all_agents_obs in unfiltered_obs.items(): is_new_episode: bool = env_id not in active_episodes episode: MultiAgentEpisode = active_episodes[env_id] if not is_new_episode: sample_collector.episode_step(episode) episode._add_agent_rewards(rewards[env_id]) # Check episode termination conditions. if dones[env_id]["__all__"] or episode.length >= horizon: hit_horizon = (episode.length >= horizon and not dones[env_id]["__all__"]) all_agents_done = True atari_metrics: List[RolloutMetrics] = _fetch_atari_metrics( base_env) if atari_metrics is not None: for m in atari_metrics: outputs.append( m._replace(custom_metrics=episode.custom_metrics)) else: outputs.append( RolloutMetrics(episode.length, episode.total_reward, dict(episode.agent_rewards), episode.custom_metrics, {}, episode.hist_data, episode.media)) # Check whether we have to create a fake-last observation # for some agents (the environment is not required to do so if # dones[__all__]=True). for ag_id in episode.get_agents(): if not episode.last_done_for( ag_id) and ag_id not in all_agents_obs: # Create a fake (all-0s) observation. obs_sp = worker.policy_map[episode.policy_for( ag_id)].observation_space obs_sp = getattr(obs_sp, "original_space", obs_sp) all_agents_obs[ag_id] = tree.map_structure( np.zeros_like, obs_sp.sample()) else: hit_horizon = False all_agents_done = False active_envs.add(env_id) # Custom observation function is applied before preprocessing. if observation_fn: all_agents_obs: Dict[AgentID, EnvObsType] = observation_fn( agent_obs=all_agents_obs, worker=worker, base_env=base_env, policies=worker.policy_map, episode=episode) if not isinstance(all_agents_obs, dict): raise ValueError( "observe() must return a dict of agent observations") # For each agent in the environment. # types: AgentID, EnvObsType for agent_id, raw_obs in all_agents_obs.items(): assert agent_id != "__all__" last_observation: EnvObsType = episode.last_observation_for( agent_id) agent_done = bool(all_agents_done or dones[env_id].get(agent_id)) # A new agent (initial obs) is already done -> Skip entirely. if last_observation is None and agent_done: continue policy_id: PolicyID = episode.policy_for(agent_id) preprocessor = _get_or_raise(worker.preprocessors, policy_id) prep_obs: EnvObsType = raw_obs if preprocessor is not None: prep_obs = preprocessor.transform(raw_obs) if log_once("prep_obs"): logger.info("Preprocessed obs: {}".format( summarize(prep_obs))) filtered_obs: EnvObsType = _get_or_raise(worker.filters, policy_id)(prep_obs) if log_once("filtered_obs"): logger.info("Filtered obs: {}".format(summarize(filtered_obs))) episode._set_last_observation(agent_id, filtered_obs) episode._set_last_raw_obs(agent_id, raw_obs) episode._set_last_done(agent_id, agent_done) # Infos from the environment. agent_infos = infos[env_id].get(agent_id, {}) episode._set_last_info(agent_id, agent_infos) # Record transition info if applicable. if last_observation is None: sample_collector.add_init_obs(episode, agent_id, env_id, policy_id, episode.length - 1, filtered_obs) else: # Add actions, rewards, next-obs to collectors. values_dict = { SampleBatch.T: episode.length - 1, SampleBatch.ENV_ID: env_id, SampleBatch.AGENT_INDEX: episode._agent_index(agent_id), # Action (slot 0) taken at timestep t. SampleBatch.ACTIONS: episode.last_action_for(agent_id), # Reward received after taking a at timestep t. SampleBatch.REWARDS: rewards[env_id].get(agent_id, 0.0), # After taking action=a, did we reach terminal? SampleBatch.DONES: (False if (no_done_at_end or (hit_horizon and soft_horizon)) else agent_done), # Next observation. SampleBatch.NEXT_OBS: filtered_obs, } # Add extra-action-fetches to collectors. pol = worker.policy_map[policy_id] for key, value in episode.last_pi_info_for(agent_id).items(): if key in pol.view_requirements: values_dict[key] = value # Env infos for this agent. if "infos" in pol.view_requirements: values_dict["infos"] = agent_infos sample_collector.add_action_reward_next_obs( episode.episode_id, agent_id, env_id, policy_id, agent_done, values_dict) if not agent_done: item = PolicyEvalData( env_id, agent_id, filtered_obs, agent_infos, None if last_observation is None else episode.rnn_state_for(agent_id), None if last_observation is None else episode.last_action_for(agent_id), rewards[env_id].get( agent_id, 0.0)) to_eval[policy_id].append(item) # Invoke the `on_episode_step` callback after the step is logged # to the episode. # Exception: The very first env.poll() call causes the env to get reset # (no step taken yet, just a single starting observation logged). # We need to skip this callback in this case. if episode.length > 0: callbacks.on_episode_step( worker=worker, base_env=base_env, policies=worker.policy_map, episode=episode, env_index=env_id) # Episode is done for all agents (dones[__all__] == True) # or we hit the horizon. if all_agents_done: is_done = dones[env_id]["__all__"] check_dones = is_done and not no_done_at_end # If, we are not allowed to pack the next episode into the same # SampleBatch (batch_mode=complete_episodes) -> Build the # MultiAgentBatch from a single episode and add it to "outputs". # Otherwise, just postprocess and continue collecting across # episodes. ma_sample_batch = sample_collector.postprocess_episode( episode, is_done=is_done or (hit_horizon and not soft_horizon), check_dones=check_dones, build=not multiple_episodes_in_batch) if ma_sample_batch: outputs.append(ma_sample_batch) # Call each (in-memory) policy's Exploration.on_episode_end # method. # Note: This may break the exploration (e.g. ParameterNoise) of # policies in the `policy_map` that have not been recently used # (and are therefore stashed to disk). However, we certainly do not # want to loop through all (even stashed) policies here as that # would counter the purpose of the LRU policy caching. for p in worker.policy_map.cache.values(): if getattr(p, "exploration", None) is not None: p.exploration.on_episode_end( policy=p, environment=base_env, episode=episode, tf_sess=p.get_session()) # Call custom on_episode_end callback. callbacks.on_episode_end( worker=worker, base_env=base_env, policies=worker.policy_map, episode=episode, env_index=env_id, ) # Horizon hit and we have a soft horizon (no hard env reset). if hit_horizon and soft_horizon: episode.soft_reset() resetted_obs: Dict[AgentID, EnvObsType] = all_agents_obs else: del active_episodes[env_id] resetted_obs: Dict[AgentID, EnvObsType] = base_env.try_reset( env_id) # Reset not supported, drop this env from the ready list. if resetted_obs is None: if horizon != float("inf"): raise ValueError( "Setting episode horizon requires reset() support " "from the environment.") # Creates a new episode if this is not async return. # If reset is async, we will get its result in some future poll. elif resetted_obs != ASYNC_RESET_RETURN: new_episode: MultiAgentEpisode = active_episodes[env_id] if observation_fn: resetted_obs: Dict[AgentID, EnvObsType] = observation_fn( agent_obs=resetted_obs, worker=worker, base_env=base_env, policies=worker.policy_map, episode=new_episode) # types: AgentID, EnvObsType for agent_id, raw_obs in resetted_obs.items(): policy_id: PolicyID = new_episode.policy_for(agent_id) preproccessor = _get_or_raise(worker.preprocessors, policy_id) prep_obs: EnvObsType = raw_obs if preproccessor is not None: prep_obs = preproccessor.transform(raw_obs) filtered_obs: EnvObsType = _get_or_raise( worker.filters, policy_id)(prep_obs) new_episode._set_last_raw_obs(agent_id, raw_obs) new_episode._set_last_observation(agent_id, filtered_obs) # Add initial obs to buffer. sample_collector.add_init_obs( new_episode, agent_id, env_id, policy_id, new_episode.length - 1, filtered_obs) item = PolicyEvalData( env_id, agent_id, filtered_obs, episode.last_info_for(agent_id) or {}, episode.rnn_state_for(agent_id), None, 0.0) to_eval[policy_id].append(item) # Try to build something. if multiple_episodes_in_batch: sample_batches = \ sample_collector.try_build_truncated_episode_multi_agent_batch() if sample_batches: outputs.extend(sample_batches) return active_envs, to_eval, outputs def _do_policy_eval( *, to_eval: Dict[PolicyID, List[PolicyEvalData]], policies: PolicyMap, sample_collector, active_episodes: Dict[EnvID, MultiAgentEpisode], ) -> Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]]: """Call compute_actions on collected episode/model data to get next action. Args: to_eval (Dict[PolicyID, List[PolicyEvalData]]): Mapping of policy IDs to lists of PolicyEvalData objects (items in these lists will be the batch's items for the model forward pass). policies (Dict[PolicyID, Policy]): Mapping from policy ID to Policy obj. sample_collector (SampleCollector): The SampleCollector object to use. active_episodes (Dict[EnvID, MultiAgentEpisode]): Mapping of Returns: eval_results: dict of policy to compute_action() outputs. """ eval_results: Dict[PolicyID, TensorStructType] = {} if log_once("compute_actions_input"): logger.info("Inputs to compute_actions():\n\n{}\n".format( summarize(to_eval))) for policy_id, eval_data in to_eval.items(): # In case the policyID has been removed from this worker, we need to # re-assign policy_id and re-lookup the Policy object to use. try: policy: Policy = _get_or_raise(policies, policy_id) except ValueError: # Important: Get the policy_mapping_fn from the active # Episode as the policy_mapping_fn from the worker may # have already been changed (mapping fn stay constant # within one episode). episode = active_episodes[eval_data[0].env_id] policy_id = episode.policy_mapping_fn( eval_data[0].agent_id, episode, worker=episode.worker) policy: Policy = _get_or_raise(policies, policy_id) input_dict = sample_collector.get_inference_input_dict(policy_id) eval_results[policy_id] = \ policy.compute_actions_from_input_dict( input_dict, timestep=policy.global_timestep, episodes=[active_episodes[t.env_id] for t in eval_data]) if log_once("compute_actions_result"): logger.info("Outputs of compute_actions():\n\n{}\n".format( summarize(eval_results))) return eval_results def _process_policy_eval_results( *, to_eval: Dict[PolicyID, List[PolicyEvalData]], eval_results: Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]], active_episodes: Dict[EnvID, MultiAgentEpisode], active_envs: Set[int], off_policy_actions: MultiEnvDict, policies: Dict[PolicyID, Policy], normalize_actions: bool, clip_actions: bool, ) -> Dict[EnvID, Dict[AgentID, EnvActionType]]: """Process the output of policy neural network evaluation. Records policy evaluation results into the given episode objects and returns replies to send back to agents in the env. Args: to_eval (Dict[PolicyID, List[PolicyEvalData]]): Mapping of policy IDs to lists of PolicyEvalData objects. eval_results (Dict[PolicyID, List]): Mapping of policy IDs to list of actions, rnn-out states, extra-action-fetches dicts. active_episodes (Dict[EnvID, MultiAgentEpisode]): Mapping from episode ID to currently ongoing MultiAgentEpisode object. active_envs (Set[int]): Set of non-terminated env ids. off_policy_actions (dict): Doubly keyed dict of env-ids -> agent ids -> off-policy-action, returned by a `BaseEnv.poll()` call. policies (Dict[PolicyID, Policy]): Mapping from policy ID to Policy. normalize_actions (bool): Whether to normalize actions to the action space's bounds. clip_actions (bool): Whether to clip actions to the action space's bounds. Returns: actions_to_send: Nested dict of env id -> agent id -> actions to be sent to Env (np.ndarrays). """ actions_to_send: Dict[EnvID, Dict[AgentID, EnvActionType]] = \ defaultdict(dict) # types: int for env_id in active_envs: actions_to_send[env_id] = {} # at minimum send empty dict # types: PolicyID, List[PolicyEvalData] for policy_id, eval_data in to_eval.items(): actions: TensorStructType = eval_results[policy_id][0] actions = convert_to_numpy(actions) rnn_out_cols: StateBatch = eval_results[policy_id][1] pi_info_cols: dict = eval_results[policy_id][2] # In case actions is a list (representing the 0th dim of a batch of # primitive actions), try converting it first. if isinstance(actions, list): actions = np.array(actions) # Store RNN state ins/outs and extra-action fetches to episode. for f_i, column in enumerate(rnn_out_cols): pi_info_cols["state_out_{}".format(f_i)] = column policy: Policy = _get_or_raise(policies, policy_id) # Split action-component batches into single action rows. actions: List[EnvActionType] = unbatch(actions) # types: int, EnvActionType for i, action in enumerate(actions): # Normalize, if necessary. if normalize_actions: action_to_send = unsquash_action(action, policy.action_space_struct) # Clip, if necessary. elif clip_actions: action_to_send = clip_action(action, policy.action_space_struct) else: action_to_send = action env_id: int = eval_data[i].env_id agent_id: AgentID = eval_data[i].agent_id episode: MultiAgentEpisode = active_episodes[env_id] episode._set_rnn_state(agent_id, [c[i] for c in rnn_out_cols]) episode._set_last_pi_info( agent_id, {k: v[i] for k, v in pi_info_cols.items()}) if env_id in off_policy_actions and \ agent_id in off_policy_actions[env_id]: episode._set_last_action(agent_id, off_policy_actions[env_id][agent_id]) else: episode._set_last_action(agent_id, action) assert agent_id not in actions_to_send[env_id] actions_to_send[env_id][agent_id] = action_to_send return actions_to_send def _fetch_atari_metrics(base_env: BaseEnv) -> List[RolloutMetrics]: """Atari games have multiple logical episodes, one per life. However, for metrics reporting we count full episodes, all lives included. """ unwrapped = base_env.get_unwrapped() if not unwrapped: return None atari_out = [] for u in unwrapped: monitor = get_wrapper_by_cls(u, MonitorEnv) if not monitor: return None for eps_rew, eps_len in monitor.next_episode_results(): atari_out.append(RolloutMetrics(eps_len, eps_rew)) return atari_out def _to_column_format(rnn_state_rows: List[List[Any]]) -> StateBatch: num_cols = len(rnn_state_rows[0]) return [[row[i] for row in rnn_state_rows] for i in range(num_cols)] def _get_or_raise(mapping: Dict[PolicyID, Union[Policy, Preprocessor, Filter]], policy_id: PolicyID) -> Union[Policy, Preprocessor, Filter]: """Returns an object under key `policy_id` in `mapping`. Args: mapping (Dict[PolicyID, Union[Policy, Preprocessor, Filter]]): The mapping dict from policy id (str) to actual object (Policy, Preprocessor, etc.). policy_id (str): The policy ID to lookup. Returns: Union[Policy, Preprocessor, Filter]: The found object. Raises: ValueError: If `policy_id` cannot be found in `mapping`. """ if policy_id not in mapping: raise ValueError( "Could not find policy for agent: PolicyID `{}` not found " "in policy map, whose keys are `{}`.".format( policy_id, mapping.keys())) return mapping[policy_id]