Source code for ray.rllib.evaluation.sampler

import logging
import queue
import time
from abc import ABCMeta, abstractmethod
from collections import defaultdict, namedtuple
from typing import (

import numpy as np
import tree  # pip install dm_tree

from ray.rllib.env.base_env import ASYNC_RESET_RETURN, BaseEnv, convert_to_base_env
from ray.rllib.evaluation.collectors.sample_collector import SampleCollector
from ray.rllib.evaluation.collectors.simple_list_collector import SimpleListCollector
from ray.rllib.evaluation.env_runner_v2 import (
from ray.rllib.evaluation.episode import Episode
from ray.rllib.evaluation.metrics import RolloutMetrics
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, concat_samples
from ray.rllib.utils.annotations import OldAPIStack, override
from ray.rllib.utils.debug import summarize
from ray.rllib.utils.deprecation import deprecation_warning, DEPRECATED_VALUE
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.numpy import convert_to_numpy, make_action_immutable
from ray.rllib.utils.spaces.space_utils import clip_action, unbatch, unsquash_action
from ray.rllib.utils.typing import (
from ray.util.debug import log_once

    from gymnasium.envs.classic_control.rendering import SimpleImageViewer

    from ray.rllib.algorithms.callbacks import DefaultCallbacks
    from ray.rllib.evaluation.observation_function import ObservationFunction
    from ray.rllib.evaluation.rollout_worker import RolloutWorker

tf1, tf, _ = try_import_tf()
logger = logging.getLogger(__name__)

_PolicyEvalData = namedtuple(
    ["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)
            ret = self[env_id] = self.default_factory(env_id)
            return ret

[docs]@OldAPIStack 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) == 0: raise RuntimeError("No data available from sampler.") return concat_samples(batches)
[docs] @abstractmethod def get_data(self) -> SampleBatchType: """Called by `` to return the next batch of data. Override this in child classes. Returns: The next batch of data. """ raise NotImplementedError
[docs] @abstractmethod def get_metrics(self) -> List[RolloutMetrics]: """Returns list of episode metrics since the last call to this method. The list will contain one RolloutMetrics object per completed episode. Returns: List of RolloutMetrics objects, one per completed episode since the last call to this method. """ raise NotImplementedError
[docs] @abstractmethod def get_extra_batches(self) -> List[SampleBatchType]: """Returns list of extra batches since the last call to this method. The list will contain all SampleBatches or MultiAgentBatches that the user has provided thus-far. Users can add these "extra batches" to an episode by calling the episode's `add_extra_batch([SampleBatchType])` method. This can be done from inside an overridden `Policy.compute_actions_from_input_dict(..., episodes)` or from a custom callback's `on_episode_[start|step|end]()` methods. Returns: List of SamplesBatches or MultiAgentBatches provided thus-far by the user since the last call to this method. """ raise NotImplementedError
[docs]@OldAPIStack class SyncSampler(SamplerInput): """Sync SamplerInput that collects experiences when `get_data()` is called."""
[docs] def __init__( self, *, worker: "RolloutWorker", env: BaseEnv, clip_rewards: Union[bool, float], rollout_fragment_length: int, count_steps_by: str = "env_steps", callbacks: "DefaultCallbacks", multiple_episodes_in_batch: bool = False, normalize_actions: bool = True, clip_actions: bool = False, observation_fn: Optional["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, horizon=DEPRECATED_VALUE, soft_horizon=DEPRECATED_VALUE, no_done_at_end=DEPRECATED_VALUE, ): """Initializes a SyncSampler instance. Args: worker: The RolloutWorker that will use this Sampler for sampling. env: Any Env object. Will be converted into an RLlib BaseEnv. clip_rewards: True for +/-1.0 clipping, actual float value for +/- value clipping. False for no clipping. rollout_fragment_length: The length of a fragment to collect before building a SampleBatch from the data and resetting the SampleBatchBuilder object. count_steps_by: One of "env_steps" (default) or "agent_steps". Use "agent_steps", if you want rollout lengths to be counted by individual agent steps. In a multi-agent env, a single env_step contains one or more agent_steps, depending on how many agents are present at any given time in the ongoing episode. callbacks: The Callbacks object to use when episode events happen during rollout. multiple_episodes_in_batch: Whether to pack multiple episodes into each batch. This guarantees batches will be exactly `rollout_fragment_length` in size. normalize_actions: Whether to normalize actions to the action space's bounds. clip_actions: Whether to clip actions according to the given action_space's bounds. observation_fn: Optional multi-agent observation func to use for preprocessing observations. sample_collector_class: An optional SampleCollector sub-class to use to collect, store, and retrieve environment-, model-, and sampler data. render: 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") if horizon != DEPRECATED_VALUE: deprecation_warning(old="horizon", error=True) if soft_horizon != DEPRECATED_VALUE: deprecation_warning(old="soft_horizon", error=True) if no_done_at_end != DEPRECATED_VALUE: deprecation_warning(old="no_done_at_end", error=True) self.base_env = convert_to_base_env(env) self.rollout_fragment_length = rollout_fragment_length self.extra_batches = queue.Queue() self.perf_stats = _PerfStats( ema_coef=worker.config.sampler_perf_stats_ema_coef, ) 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 if worker.config.enable_connectors: # Keep a reference to the underlying EnvRunnerV2 instance for # unit testing purpose. self._env_runner_obj = EnvRunnerV2( worker=worker, base_env=self.base_env, multiple_episodes_in_batch=multiple_episodes_in_batch, callbacks=callbacks, perf_stats=self.perf_stats, rollout_fragment_length=rollout_fragment_length, count_steps_by=count_steps_by, render=self.render, ) self._env_runner = else: # Create the rollout generator to use for calls to `get_data()`. self._env_runner = _env_runner( worker, self.base_env, self.extra_batches.put, normalize_actions, clip_actions, multiple_episodes_in_batch, callbacks, self.perf_stats, 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
@OldAPIStack def _env_runner( worker: "RolloutWorker", base_env: BaseEnv, extra_batch_callback: Callable[[SampleBatchType], None], normalize_actions: bool, clip_actions: bool, multiple_episodes_in_batch: bool, callbacks: "DefaultCallbacks", perf_stats: _PerfStats, observation_fn: "ObservationFunction", sample_collector: Optional[SampleCollector] = None, render: bool = None, ) -> Iterator[SampleBatchType]: """This implements the common experience collection logic. Args: worker: Reference to the current rollout worker. base_env: Env implementing BaseEnv. extra_batch_callback: function to send extra batch data to. multiple_episodes_in_batch: Whether to pack multiple episodes into each batch. This guarantees batches will be exactly `rollout_fragment_length` in size. normalize_actions: Whether to normalize actions to the action space's bounds. clip_actions: Whether to clip actions to the space range. callbacks: User callbacks to run on episode events. perf_stats: Record perf stats into this object. observation_fn: Optional multi-agent observation func to use for preprocessing observations. sample_collector: An optional SampleCollector object to use. render: Whether to try to render the environment after each step. Yields: 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 def _new_episode(env_id): episode = Episode( worker.policy_map, worker.policy_mapping_fn, # SimpleListCollector will find or create a # simple_list_collector._PolicyCollector as batch_builder # for this episode later. Here we simply provide a None factory. lambda: None, # batch_builder_factory extra_batch_callback, env_id=env_id, worker=worker, ) return episode active_episodes: Dict[EnvID, Episode] = _NewEpisodeDefaultDict(_new_episode) # Before the very first poll (this will reset all vector sub-environments): # Call custom `before_sub_environment_reset` callbacks for all sub-environments. for env_id, sub_env in base_env.get_sub_environments(as_dict=True).items(): _create_episode(active_episodes, env_id, callbacks, worker, base_env) while True: perf_stats.incr("iters", 1) t0 = time.time() # Get observations from all ready agents. # types: MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, ... ( unfiltered_obs, rewards, terminateds, truncateds, infos, off_policy_actions, ) = base_env.poll() env_poll_time = time.time() - t0 if log_once("env_returns"):"Raw obs from env: {}".format(summarize(unfiltered_obs)))"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, terminateds=terminateds, truncateds=truncateds, infos=infos, multiple_episodes_in_batch=multiple_episodes_in_batch, callbacks=callbacks, observation_fn=observation_fn, sample_collector=sample_collector, ) perf_stats.incr("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.incr("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.incr("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.incr("env_wait_time", env_poll_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 gymnasium.envs.classic_control.rendering import ( SimpleImageViewer, ) simple_image_viewer = SimpleImageViewer() except (ImportError, ModuleNotFoundError): render = False # disable rendering logger.warning( "Could not import gymnasium.envs.classic_control." "rendering! Try `pip install gymnasium[all]`." ) if simple_image_viewer: simple_image_viewer.imshow(rendered) elif rendered not in [True, False, None]: raise ValueError( f"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.incr("env_render_time", time.time() - t5) @OldAPIStack def _process_observations( *, worker: "RolloutWorker", base_env: BaseEnv, active_episodes: Dict[EnvID, Episode], unfiltered_obs: Dict[EnvID, Dict[AgentID, EnvObsType]], rewards: Dict[EnvID, Dict[AgentID, float]], terminateds: Dict[EnvID, Dict[AgentID, bool]], truncateds: Dict[EnvID, Dict[AgentID, bool]], infos: Dict[EnvID, Dict[AgentID, EnvInfoDict]], multiple_episodes_in_batch: bool, callbacks: "DefaultCallbacks", 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: Reference to the current rollout worker. base_env: Env implementing BaseEnv. active_episodes: Mapping from episode ID to currently ongoing Episode object. unfiltered_obs: Doubly keyed dict of env-ids -> agent ids -> unfiltered observation tensor, returned by a `BaseEnv.poll()` call. rewards: Doubly keyed dict of env-ids -> agent ids -> rewards tensor, returned by a `BaseEnv.poll()` call. terminateds: Doubly keyed dict of env-ids -> agent ids -> boolean `terminated` flags, returned by a `BaseEnv.poll()` call. truncateds: Doubly keyed dict of env-ids -> agent ids -> boolean `truncated` flags, returned by a `BaseEnv.poll()` call. infos: Doubly keyed dict of env-ids -> agent ids -> info dicts, returned by a `BaseEnv.poll()` call. multiple_episodes_in_batch: Whether to pack multiple episodes into each batch. This guarantees batches will be exactly `rollout_fragment_length` in size. callbacks: User callbacks to run on episode events. observation_fn: Optional multi-agent observation func to use for preprocessing observations. sample_collector: The SampleCollector object used to store and retrieve environment samples. Returns: Tuple consisting of 1) active_envs: Set of non-terminated env ids. 2) to_eval: Map of policy_id to list of agent _PolicyEvalData. 3) 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(): episode: Episode = active_episodes[env_id] # Check for env_id having returned an error instead of a multi-agent obs dict. # This is how our BaseEnv can tell the caller to `poll()` that one of its # sub-environments is faulty and should be restarted (and the ongoing episode # should not be used for training). if isinstance(all_agents_obs, Exception): episode.is_faulty = True assert terminateds[env_id]["__all__"] is True, ( f"ERROR: When a sub-environment (env-id {env_id}) returns an error as " "observation, the terminateds[__all__] flag must also be set to True!" ) # This will be filled with dummy observations below. all_agents_obs = {} # Add init obs and infos (from the call to `reset/try_reset`) to episode. for aid, obs in all_agents_obs.items(): episode._set_last_raw_obs(aid, obs) common_infos = infos[env_id].get("__common__", {}) episode._set_last_info("__common__", common_infos) for aid, info in infos[env_id].items(): episode._set_last_info(aid, info) # Episode is brand new. if episode.started is False: # Call the episode start callback(s). _call_on_episode_start(episode, env_id, callbacks, worker, base_env) else: sample_collector.episode_step(episode) episode._add_agent_rewards(rewards[env_id]) # Check episode termination conditions. if terminateds[env_id]["__all__"] or truncateds[env_id]["__all__"]: all_agents_done = True # Check whether we have to create a fake-last observation # for some agents (the environment is not required to do so if # terminateds[__all__]=True or truncateds[__all__]=True). for ag_id in episode.get_agents(): if ( not episode.last_terminated_for(ag_id) and not episode.last_truncated_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: 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_terminated = bool( terminateds[env_id]["__all__"] or terminateds[env_id].get(agent_id) ) agent_truncated = bool( truncateds[env_id]["__all__"] or truncateds[env_id].get(agent_id, False) ) # A new agent (initial obs) is already done -> Skip entirely. if last_observation is None and (agent_terminated or agent_truncated): 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"):"Preprocessed obs: {}".format(summarize(prep_obs))) filtered_obs: EnvObsType = _get_or_raise(worker.filters, policy_id)( prep_obs ) if log_once("filtered_obs"):"Filtered obs: {}".format(summarize(filtered_obs))) episode._set_last_observation(agent_id, filtered_obs) episode._set_last_terminated(agent_id, agent_terminated) episode._set_last_truncated(agent_id, agent_truncated) agent_infos = infos[env_id].get(agent_id, {}) # Record transition info if applicable. if last_observation is None: sample_collector.add_init_obs( episode=episode, agent_id=agent_id, env_id=env_id, policy_id=policy_id, init_obs=filtered_obs, init_infos=agent_infos, t=episode.length - 1, ) 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 terminate the episode? SampleBatch.TERMINATEDS: agent_terminated, # Was the episode truncated artificially # (e.g. b/c of some time limit)? SampleBatch.TRUNCATEDS: agent_truncated, # Next observation. SampleBatch.NEXT_OBS: filtered_obs, } # Add extra-action-fetches (policy-inference infos) to # collectors. pol = worker.policy_map[policy_id] for key, value in episode.last_extra_action_outs_for(agent_id).items(): if key in pol.view_requirements: values_dict[key] = value # Env infos for this agent. if SampleBatch.INFOS in pol.view_requirements: values_dict[SampleBatch.INFOS] = agent_infos sample_collector.add_action_reward_next_obs( episode.episode_id, agent_id, env_id, policy_id, agent_terminated or agent_truncated, values_dict, ) if not agent_terminated and not agent_truncated: 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 not episode.is_faulty and 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 terminated for all agents (terminateds[__all__] == True or # truncateds[__all__] == True). if all_agents_done: # 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. # If an episode was marked faulty, perform regular postprocessing # (to e.g. properly flush and clean up the SampleCollector's buffers), # but then discard the entire batch and don't return it. ma_sample_batch = None if not episode.is_faulty or episode.length > 0: ma_sample_batch = sample_collector.postprocess_episode( episode, is_done=True, check_dones=True, build=episode.is_faulty or not multiple_episodes_in_batch, ) if not episode.is_faulty: # 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, ) # Now that all callbacks are done and users had the chance to add custom # metrics based on the last observation in the episode, finish up metrics # object and append to `outputs`. atari_metrics: List[RolloutMetrics] = _fetch_atari_metrics(base_env) if not episode.is_faulty: if atari_metrics is not None: for m in atari_metrics: outputs.append( m._replace( custom_metrics=episode.custom_metrics, hist_data=episode.hist_data, ) ) else: outputs.append( RolloutMetrics( episode.length, episode.total_reward, dict(episode.agent_rewards), episode.custom_metrics, {}, episode.hist_data,, ) ) else: # Add metrics about a faulty episode. outputs.append(RolloutMetrics(episode_faulty=True)) # Only after the RolloutMetrics were appended, append the collected sample # batch, if any. if not episode.is_faulty and ma_sample_batch: outputs.append(ma_sample_batch) # Terminated: Try to reset the sub environment. # Clean up old finished episode. del active_episodes[env_id] # Create a new episode and call `on_episode_created` callback(s). _create_episode(active_episodes, env_id, callbacks, worker, base_env) # The sub environment at index `env_id` might throw an exception # during the following `try_reset()` attempt. If configured with # `restart_failed_sub_environments=True`, the BaseEnv will restart # the affected sub environment (create a new one using its c'tor) and # must reset the recreated sub env right after that. # Should the sub environment fail indefinitely during these # repeated reset attempts, the entire worker will be blocked. # This would be ok, b/c the alternative would be the worker crashing # entirely. while True: resetted_obs, resetted_infos = base_env.try_reset(env_id) if resetted_obs is None or not isinstance( resetted_obs[env_id], Exception ): break else: # Failed to reset, add metrics about a faulty episode. outputs.append(RolloutMetrics(episode_faulty=True)) # Creates a new episode if this is not async return. # If reset is async, we will get its result in some future poll. if resetted_obs is not None and resetted_obs != ASYNC_RESET_RETURN: new_episode: Episode = active_episodes[env_id] resetted_obs = resetted_obs[env_id] resetted_infos = resetted_infos[env_id] # Add init obs and infos (from the call to `reset/try_reset`) to # episode. for aid, obs in resetted_obs.items(): new_episode._set_last_raw_obs(aid, obs) common_infos = resetted_infos.get("__common__", {}) new_episode._set_last_info("__common__", common_infos) for aid, info in resetted_infos.items(): new_episode._set_last_info(aid, info) _call_on_episode_start(new_episode, env_id, callbacks, worker, base_env) _assert_episode_not_faulty(new_episode) 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_observation(agent_id, filtered_obs) # Add initial obs to buffer. sample_collector.add_init_obs( episode=new_episode, agent_id=agent_id, env_id=env_id, policy_id=policy_id, init_obs=filtered_obs, init_infos=resetted_infos, t=new_episode.length - 1, ) item = _PolicyEvalData( env_id, agent_id, filtered_obs, new_episode.last_info_for(agent_id) or {}, new_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 @OldAPIStack def _do_policy_eval( *, to_eval: Dict[PolicyID, List[_PolicyEvalData]], policies: PolicyMap, sample_collector: SampleCollector, active_episodes: Dict[EnvID, Episode], ) -> Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]]: """Call compute_actions on collected episode/model data to get next action. Args: to_eval: 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: Mapping from policy ID to Policy obj. sample_collector: The SampleCollector object to use. active_episodes: Mapping of EnvID to its currently active episode. Returns: Dict mapping PolicyIDs to compute_actions_from_input_dict() outputs. """ eval_results: Dict[PolicyID, TensorStructType] = {} if log_once("compute_actions_input"):"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] _assert_episode_not_faulty(episode) 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"): "Outputs of compute_actions():\n\n{}\n".format(summarize(eval_results)) ) return eval_results @OldAPIStack def _process_policy_eval_results( *, to_eval: Dict[PolicyID, List[_PolicyEvalData]], eval_results: Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]], active_episodes: Dict[EnvID, Episode], 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: Mapping of policy IDs to lists of _PolicyEvalData objects. eval_results: Mapping of policy IDs to list of actions, rnn-out states, extra-action-fetches dicts. active_episodes: Mapping from episode ID to currently ongoing Episode object. active_envs: Set of non-terminated env ids. off_policy_actions: Doubly keyed dict of env-ids -> agent ids -> off-policy-action, returned by a `BaseEnv.poll()` call. policies: Mapping from policy ID to Policy. normalize_actions: Whether to normalize actions to the action space's bounds. clip_actions: Whether to clip actions to the action space's bounds. Returns: 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] extra_action_out_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): extra_action_out_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: Episode = active_episodes[env_id] _assert_episode_not_faulty(episode) episode._set_rnn_state( agent_id, tree.map_structure(lambda x: x[i], rnn_out_cols) ) episode._set_last_extra_action_outs( agent_id, tree.map_structure(lambda x: x[i], extra_action_out_cols) ) 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] # Flag actions as immutable to notify the user when trying to change it # and to avoid hardly traceable errors. tree.traverse(make_action_immutable, action_to_send, top_down=False) actions_to_send[env_id][agent_id] = action_to_send return actions_to_send @OldAPIStack def _create_episode(active_episodes, env_id, callbacks, worker, base_env): # Make sure we are really creating a new episode here. assert env_id not in active_episodes # Create a new episode under the given `env_id` and call the # `on_episode_created` callbacks. new_episode = active_episodes[env_id] # Call `on_episode_created()` callback. callbacks.on_episode_created( worker=worker, base_env=base_env, policies=worker.policy_map, env_index=env_id, episode=new_episode, ) return new_episode @OldAPIStack def _call_on_episode_start(episode, env_id, callbacks, worker, base_env): # 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, ) episode.started = True 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 _assert_episode_not_faulty(episode): if episode.is_faulty: raise AssertionError( "Episodes marked as `faulty` should not be kept in the " f"`active_episodes` map! Episode ID={episode.episode_id}." )