algorithm.py

import concurrent
import copy
import functools
import logging
import math
import os
import pickle
import tempfile
import time
import importlib
from collections import defaultdict
from datetime import datetime
from typing import (
    Callable,
    Container,
    DefaultDict,
    Dict,
    List,
    Optional,
    Set,
    Tuple,
    Type,
    Union,
)

import gym
import numpy as np
import pkg_resources
from packaging import version

import ray
from ray._private.usage.usage_lib import TagKey, record_extra_usage_tag
from ray.actor import ActorHandle
from ray.exceptions import GetTimeoutError, RayActorError, RayError
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
from ray.rllib.algorithms.callbacks import DefaultCallbacks
from ray.rllib.algorithms.registry import ALGORITHMS as ALL_ALGORITHMS
from ray.rllib.env.env_context import EnvContext
from ray.rllib.env.utils import _gym_env_creator
from ray.rllib.evaluation.episode import Episode
from ray.rllib.evaluation.metrics import (
    collect_episodes,
    collect_metrics,
    summarize_episodes,
)
from ray.rllib.evaluation.rollout_worker import RolloutWorker
from ray.rllib.evaluation.worker_set import WorkerSet
from ray.rllib.execution.common import (
    STEPS_TRAINED_THIS_ITER_COUNTER,  # TODO: Backward compatibility.
)
from ray.rllib.execution.parallel_requests import AsyncRequestsManager
from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step
from ray.rllib.offline import get_offline_io_resource_bundles
from ray.rllib.offline.estimators import (
    OffPolicyEstimator,
    ImportanceSampling,
    WeightedImportanceSampling,
    DirectMethod,
    DoublyRobust,
)
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch, concat_samples
from ray.rllib.utils import deep_update, FilterManager, merge_dicts
from ray.rllib.utils.annotations import (
    DeveloperAPI,
    ExperimentalAPI,
    OverrideToImplementCustomLogic,
    OverrideToImplementCustomLogic_CallToSuperRecommended,
    PublicAPI,
    override,
)
from ray.rllib.utils.debug import update_global_seed_if_necessary
from ray.rllib.utils.deprecation import (
    DEPRECATED_VALUE,
    Deprecated,
    deprecation_warning,
)
from ray.rllib.utils.error import ERR_MSG_INVALID_ENV_DESCRIPTOR, EnvError
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.from_config import from_config
from ray.rllib.utils.metrics import (
    NUM_AGENT_STEPS_SAMPLED,
    NUM_AGENT_STEPS_SAMPLED_THIS_ITER,
    NUM_AGENT_STEPS_TRAINED,
    NUM_ENV_STEPS_SAMPLED,
    NUM_ENV_STEPS_SAMPLED_THIS_ITER,
    NUM_ENV_STEPS_TRAINED,
    SYNCH_WORKER_WEIGHTS_TIMER,
    TRAINING_ITERATION_TIMER,
)
from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
from ray.rllib.utils.pre_checks.multi_agent import check_multi_agent
from ray.rllib.utils.replay_buffers import MultiAgentReplayBuffer
from ray.rllib.utils.spaces import space_utils
from ray.rllib.utils.typing import (
    AgentID,
    AlgorithmConfigDict,
    EnvCreator,
    EnvInfoDict,
    EnvType,
    EpisodeID,
    PartialAlgorithmConfigDict,
    PolicyID,
    PolicyState,
    ResultDict,
    SampleBatchType,
    TensorStructType,
    TensorType,
)
from ray.tune.logger import Logger, UnifiedLogger
from ray.tune.registry import ENV_CREATOR, _global_registry
from ray.tune.resources import Resources
from ray.tune.result import DEFAULT_RESULTS_DIR
from ray.tune.trainable import Trainable
from ray.tune.experiment.trial import ExportFormat
from ray.tune.execution.placement_groups import PlacementGroupFactory
from ray.util import log_once
from ray.util.timer import _Timer

tf1, tf, tfv = try_import_tf()

logger = logging.getLogger(__name__)


@DeveloperAPI
def with_common_config(extra_config: PartialAlgorithmConfigDict) -> AlgorithmConfigDict:
    """Returns the given config dict merged with common agent confs.

    Args:
        extra_config: A user defined partial config
            which will get merged with a default AlgorithmConfig() object and returned
            as plain python dict.

    Returns:
        AlgorithmConfigDict: The merged config dict resulting from AlgorithmConfig()
            plus `extra_config`.
    """
    return Algorithm.merge_trainer_configs(
        AlgorithmConfig().to_dict(), extra_config, _allow_unknown_configs=True
    )


@PublicAPI
class Algorithm(Trainable):
    """An RLlib algorithm responsible for optimizing one or more Policies.

    Algorithms contain a WorkerSet under `self.workers`. A WorkerSet is
    normally composed of a single local worker
    (self.workers.local_worker()), used to compute and apply learning updates,
    and optionally one or more remote workers (self.workers.remote_workers()),
    used to generate environment samples in parallel.

    Each worker (remotes or local) contains a PolicyMap, which itself
    may contain either one policy for single-agent training or one or more
    policies for multi-agent training. Policies are synchronized
    automatically from time to time using ray.remote calls. The exact
    synchronization logic depends on the specific algorithm used,
    but this usually happens from local worker to all remote workers and
    after each training update.

    You can write your own Algorithm classes by sub-classing from `Algorithm`
    or any of its built-in sub-classes.
    This allows you to override the `execution_plan` method to implement
    your own algorithm logic. You can find the different built-in
    algorithms' execution plans in their respective main py files,
    e.g. rllib.algorithms.dqn.dqn.py or rllib.algorithms.impala.impala.py.

    The most important API methods a Algorithm exposes are `train()`,
    `evaluate()`, `save()` and `restore()`.
    """

    # Whether to allow unknown top-level config keys.
    _allow_unknown_configs = False

    # List of top-level keys with value=dict, for which new sub-keys are
    # allowed to be added to the value dict.
    _allow_unknown_subkeys = [
        "tf_session_args",
        "local_tf_session_args",
        "env_config",
        "model",
        "optimizer",
        "multiagent",
        "custom_resources_per_worker",
        "evaluation_config",
        "exploration_config",
        "replay_buffer_config",
        "extra_python_environs_for_worker",
        "input_config",
        "output_config",
    ]

    # List of top level keys with value=dict, for which we always override the
    # entire value (dict), iff the "type" key in that value dict changes.
    _override_all_subkeys_if_type_changes = [
        "exploration_config",
        "replay_buffer_config",
    ]

    # List of keys that are always fully overridden if present in any dict or sub-dict
    _override_all_key_list = ["off_policy_estimation_methods"]

    _progress_metrics = [
        "episode_reward_mean",
        "evaluation/episode_reward_mean",
        "num_env_steps_sampled",
        "num_env_steps_trained",
    ]

    @PublicAPI
    def __init__(
        self,
        config: Optional[Union[PartialAlgorithmConfigDict, AlgorithmConfig]] = None,
        env: Optional[Union[str, EnvType]] = None,
        logger_creator: Optional[Callable[[], Logger]] = None,
        **kwargs,
    ):
        """Initializes an Algorithm instance.

        Args:
            config: Algorithm-specific configuration dict.
            env: Name of the environment to use (e.g. a gym-registered str),
                a full class path (e.g.
                "ray.rllib.examples.env.random_env.RandomEnv"), or an Env
                class directly. Note that this arg can also be specified via
                the "env" key in `config`.
            logger_creator: Callable that creates a ray.tune.Logger
                object. If unspecified, a default logger is created.
            **kwargs: Arguments passed to the Trainable base class.

        """

        # User provided (partial) config (this may be w/o the default
        # Trainer's Config object). Will get merged with AlgorithmConfig()
        # in self.setup().
        config = config or {}
        # Resolve AlgorithmConfig into a plain dict.
        # TODO: In the future, only support AlgorithmConfig objects here.
        if isinstance(config, AlgorithmConfig):
            config = config.to_dict()

        # Convert `env` provided in config into a concrete env creator callable, which
        # takes an EnvContext (config dict) as arg and returning an RLlib supported Env
        # type (e.g. a gym.Env).
        self._env_id, self.env_creator = self._get_env_id_and_creator(
            env or config.get("env"), config
        )
        env_descr = (
            self._env_id.__name__ if isinstance(self._env_id, type) else self._env_id
        )

        # Placeholder for a local replay buffer instance.
        self.local_replay_buffer = None

        # Create a default logger creator if no logger_creator is specified
        if logger_creator is None:
            # Default logdir prefix containing the agent's name and the
            # env id.
            timestr = datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
            logdir_prefix = "{}_{}_{}".format(str(self), env_descr, timestr)
            if not os.path.exists(DEFAULT_RESULTS_DIR):
                os.makedirs(DEFAULT_RESULTS_DIR)
            logdir = tempfile.mkdtemp(prefix=logdir_prefix, dir=DEFAULT_RESULTS_DIR)

            # Allow users to more precisely configure the created logger
            # via "logger_config.type".
            if config.get("logger_config") and "type" in config["logger_config"]:

                def default_logger_creator(config):
                    """Creates a custom logger with the default prefix."""
                    cfg = config["logger_config"].copy()
                    cls = cfg.pop("type")
                    # Provide default for logdir, in case the user does
                    # not specify this in the "logger_config" dict.
                    logdir_ = cfg.pop("logdir", logdir)
                    return from_config(cls=cls, _args=[cfg], logdir=logdir_)

            # If no `type` given, use tune's UnifiedLogger as last resort.
            else:

                def default_logger_creator(config):
                    """Creates a Unified logger with the default prefix."""
                    return UnifiedLogger(config, logdir, loggers=None)

            logger_creator = default_logger_creator

        # Metrics-related properties.
        self._timers = defaultdict(_Timer)
        self._counters = defaultdict(int)
        self._episode_history = []
        self._episodes_to_be_collected = []
        self._remote_workers_for_metrics = []

        # Evaluation WorkerSet and metrics last returned by `self.evaluate()`.
        self.evaluation_workers: Optional[WorkerSet] = None
        # If evaluation duration is "auto", use a AsyncRequestsManager to be more
        # robust against eval worker failures.
        self._evaluation_async_req_manager: Optional[AsyncRequestsManager] = None
        # Initialize common evaluation_metrics to nan, before they become
        # available. We want to make sure the metrics are always present
        # (although their values may be nan), so that Tune does not complain
        # when we use these as stopping criteria.
        self.evaluation_metrics = {
            "evaluation": {
                "episode_reward_max": np.nan,
                "episode_reward_min": np.nan,
                "episode_reward_mean": np.nan,
            }
        }

        super().__init__(config=config, logger_creator=logger_creator, **kwargs)

        # Check, whether `training_iteration` is still a tune.Trainable property
        # and has not been overridden by the user in the attempt to implement the
        # algos logic (this should be done now inside `training_step`).
        try:
            assert isinstance(self.training_iteration, int)
        except AssertionError:
            raise AssertionError(
                "Your Algorithm's `training_iteration` seems to be overridden by your "
                "custom training logic! To solve this problem, simply rename your "
                "`self.training_iteration()` method into `self.training_step`."
            )

    @OverrideToImplementCustomLogic
    @classmethod
    def get_default_config(cls) -> AlgorithmConfigDict:
        return AlgorithmConfig().to_dict()

    @OverrideToImplementCustomLogic_CallToSuperRecommended
    @override(Trainable)
    def setup(self, config: PartialAlgorithmConfigDict):

        # Setup our config: Merge the user-supplied config (which could
        # be a partial config dict with the class' default).
        self.config = self.merge_trainer_configs(
            self.get_default_config(), config, self._allow_unknown_configs
        )
        self.config["env"] = self._env_id

        # Validate the framework settings in config.
        self.validate_framework(self.config)

        # Set Trainer's seed after we have - if necessary - enabled
        # tf eager-execution.
        update_global_seed_if_necessary(self.config["framework"], self.config["seed"])

        self.validate_config(self.config)
        self._record_usage(self.config)

        self.callbacks = self.config["callbacks"]()
        log_level = self.config.get("log_level")
        if log_level in ["WARN", "ERROR"]:
            logger.info(
                "Current log_level is {}. For more information, "
                "set 'log_level': 'INFO' / 'DEBUG' or use the -v and "
                "-vv flags.".format(log_level)
            )
        if self.config.get("log_level"):
            logging.getLogger("ray.rllib").setLevel(self.config["log_level"])

        # Create local replay buffer if necessary.
        self.local_replay_buffer = self._create_local_replay_buffer_if_necessary(
            self.config
        )

        # Create a dict, mapping ActorHandles to sets of open remote
        # requests (object refs). This way, we keep track, of which actors
        # inside this Trainer (e.g. a remote RolloutWorker) have
        # already been sent how many (e.g. `sample()`) requests.
        self.remote_requests_in_flight: DefaultDict[
            ActorHandle, Set[ray.ObjectRef]
        ] = defaultdict(set)

        self.workers: Optional[WorkerSet] = None
        self.train_exec_impl = None

        # Offline RL settings.
        input_evaluation = self.config.get("input_evaluation")
        if input_evaluation is not None and input_evaluation is not DEPRECATED_VALUE:
            ope_dict = {str(ope): {"type": ope} for ope in input_evaluation}
            deprecation_warning(
                old="config.input_evaluation={}".format(input_evaluation),
                new='config["evaluation_config"]'
                '["off_policy_estimation_methods"]={}'.format(
                    ope_dict,
                ),
                error=False,
                help="Running OPE during training is not recommended.",
            )
            self.config["off_policy_estimation_methods"] = ope_dict

        # Deprecated way of implementing Trainer sub-classes (or "templates"
        # via the `build_trainer` utility function).
        # Instead, sub-classes should override the Trainable's `setup()`
        # method and call super().setup() from within that override at some
        # point.
        # Old design: Override `Trainer._init`.
        _init = False
        try:
            self._init(self.config, self.env_creator)
            _init = True
        # New design: Override `Trainable.setup()` (as indented by tune.Trainable)
        # and do or don't call `super().setup()` from within your override.
        # By default, `super().setup()` will create both worker sets:
        # "rollout workers" for collecting samples for training and - if
        # applicable - "evaluation workers" for evaluation runs in between or
        # parallel to training.
        # TODO: Deprecate `_init()` and remove this try/except block.
        except NotImplementedError:
            pass

        # Only if user did not override `_init()`:
        if _init is False:
            # - Create rollout workers here automatically.
            # - Run the execution plan to create the local iterator to `next()`
            #   in each training iteration.
            # This matches the behavior of using `build_trainer()`, which
            # has been deprecated.
            try:
                self.workers = WorkerSet(
                    env_creator=self.env_creator,
                    validate_env=self.validate_env,
                    policy_class=self.get_default_policy_class(self.config),
                    trainer_config=self.config,
                    num_workers=self.config["num_workers"],
                    local_worker=True,
                    logdir=self.logdir,
                )
            # WorkerSet creation possibly fails, if some (remote) workers cannot
            # be initialized properly (due to some errors in the RolloutWorker's
            # constructor).
            except RayActorError as e:
                # In case of an actor (remote worker) init failure, the remote worker
                # may still exist and will be accessible, however, e.g. calling
                # its `sample.remote()` would result in strange "property not found"
                # errors.
                if e.actor_init_failed:
                    # Raise the original error here that the RolloutWorker raised
                    # during its construction process. This is to enforce transparency
                    # for the user (better to understand the real reason behind the
                    # failure).
                    # - e.args[0]: The RayTaskError (inside the caught RayActorError).
                    # - e.args[0].args[2]: The original Exception (e.g. a ValueError due
                    # to a config mismatch) thrown inside the actor.
                    raise e.args[0].args[2]
                # In any other case, raise the RayActorError as-is.
                else:
                    raise e
            # By default, collect metrics for all remote workers.
            self._remote_workers_for_metrics = self.workers.remote_workers()

            # Function defining one single training iteration's behavior.
            if self.config["_disable_execution_plan_api"]:
                # Ensure remote workers are initially in sync with the local worker.
                self.workers.sync_weights()
            # LocalIterator-creating "execution plan".
            # Only call this once here to create `self.train_exec_impl`,
            # which is a ray.util.iter.LocalIterator that will be `next`'d
            # on each training iteration.
            else:
                self.train_exec_impl = self.execution_plan(
                    self.workers, self.config, **self._kwargs_for_execution_plan()
                )

            # Now that workers have been created, update our policies
            # dict in config[multiagent] (with the correct original/
            # unpreprocessed spaces).
            self.config["multiagent"][
                "policies"
            ] = self.workers.local_worker().policy_dict

        # Evaluation WorkerSet setup.
        # User would like to setup a separate evaluation worker set.

        # Update with evaluation settings:
        user_eval_config = copy.deepcopy(self.config["evaluation_config"])

        # Assert that user has not unset "in_evaluation".
        assert (
            "in_evaluation" not in user_eval_config
            or user_eval_config["in_evaluation"] is True
        )

        # Merge user-provided eval config with the base config. This makes sure
        # the eval config is always complete, no matter whether we have eval
        # workers or perform evaluation on the (non-eval) local worker.
        eval_config = merge_dicts(self.config, user_eval_config)
        self.config["evaluation_config"] = eval_config

        if self.config.get("evaluation_num_workers", 0) > 0 or self.config.get(
            "evaluation_interval"
        ):
            logger.debug(f"Using evaluation_config: {user_eval_config}.")

            # Validate evaluation config.
            self.validate_config(eval_config)

            # Set the `in_evaluation` flag.
            eval_config["in_evaluation"] = True

            # Evaluation duration unit: episodes.
            # Switch on `complete_episode` rollouts. Also, make sure
            # rollout fragments are short so we never have more than one
            # episode in one rollout.
            if eval_config["evaluation_duration_unit"] == "episodes":
                eval_config.update(
                    {
                        "batch_mode": "complete_episodes",
                        "rollout_fragment_length": 1,
                    }
                )
            # Evaluation duration unit: timesteps.
            # - Set `batch_mode=truncate_episodes` so we don't perform rollouts
            #   strictly along episode borders.
            # Set `rollout_fragment_length` such that desired steps are divided
            # equally amongst workers or - in "auto" duration mode - set it
            # to a reasonably small number (10), such that a single `sample()`
            # call doesn't take too much time and we can stop evaluation as soon
            # as possible after the train step is completed.
            else:
                eval_config.update(
                    {
                        "batch_mode": "truncate_episodes",
                        "rollout_fragment_length": 10
                        if self.config["evaluation_duration"] == "auto"
                        else int(
                            math.ceil(
                                self.config["evaluation_duration"]
                                / (self.config["evaluation_num_workers"] or 1)
                            )
                        ),
                    }
                )

            self.config["evaluation_config"] = eval_config

            _, env_creator = self._get_env_id_and_creator(
                eval_config.get("env"), eval_config
            )

            # Create a separate evaluation worker set for evaluation.
            # If evaluation_num_workers=0, use the evaluation set's local
            # worker for evaluation, otherwise, use its remote workers
            # (parallelized evaluation).
            self.evaluation_workers: WorkerSet = WorkerSet(
                env_creator=env_creator,
                validate_env=None,
                policy_class=self.get_default_policy_class(self.config),
                trainer_config=eval_config,
                num_workers=self.config["evaluation_num_workers"],
                # Don't even create a local worker if num_workers > 0.
                local_worker=False,
                logdir=self.logdir,
            )

            if self.config["enable_async_evaluation"]:
                self._evaluation_async_req_manager = AsyncRequestsManager(
                    workers=self.evaluation_workers.remote_workers(),
                    max_remote_requests_in_flight_per_worker=1,
                    return_object_refs=True,
                )
                self._evaluation_weights_seq_number = 0

        self.reward_estimators: Dict[str, OffPolicyEstimator] = {}
        ope_types = {
            "is": ImportanceSampling,
            "wis": WeightedImportanceSampling,
            "dm": DirectMethod,
            "dr": DoublyRobust,
        }
        for name, method_config in self.config["off_policy_estimation_methods"].items():
            method_type = method_config.pop("type")
            if method_type in ope_types:
                deprecation_warning(
                    old=method_type,
                    new=str(ope_types[method_type]),
                    error=False,
                )
                method_type = ope_types[method_type]
            elif isinstance(method_type, str):
                logger.log(0, "Trying to import from string: " + method_type)
                mod, obj = method_type.rsplit(".", 1)
                mod = importlib.import_module(mod)
                method_type = getattr(mod, obj)
            if isinstance(method_type, type) and issubclass(
                method_type, OffPolicyEstimator
            ):
                policy = self.get_policy()
                gamma = self.config["gamma"]
                self.reward_estimators[name] = method_type(
                    policy, gamma, **method_config
                )
            else:
                raise ValueError(
                    f"Unknown off_policy_estimation type: {method_type}! Must be "
                    "either a class path or a sub-class of ray.rllib."
                    "offline.estimators.off_policy_estimator::OffPolicyEstimator"
                )

        # Run `on_algorithm_init` callback after initialization is done.
        self.callbacks.on_algorithm_init(algorithm=self)

    # TODO: Deprecated: In your sub-classes of Trainer, override `setup()`
    #  directly and call super().setup() from within it if you would like the
    #  default setup behavior plus some own setup logic.
    #  If you don't need the env/workers/config/etc.. setup for you by super,
    #  simply do not call super().setup() from your overridden method.
    def _init(self, config: AlgorithmConfigDict, env_creator: EnvCreator) -> None:
        raise NotImplementedError

    @OverrideToImplementCustomLogic
    def get_default_policy_class(self, config: AlgorithmConfigDict) -> Type[Policy]:
        """Returns a default Policy class to use, given a config.

        This class will be used inside RolloutWorkers' PolicyMaps in case
        the policy class is not provided by the user in any single- or
        multi-agent PolicySpec.

        This method is experimental and currently only used, iff the Trainer
        class was not created using the `build_trainer` utility and if
        the Trainer sub-class does not override `_init()` and create it's
        own WorkerSet in `_init()`.
        """
        return getattr(self, "_policy_class", None)

    @override(Trainable)
    def step(self) -> ResultDict:
        """Implements the main `Trainer.train()` logic.

        Takes n attempts to perform a single training step. Thereby
        catches RayErrors resulting from worker failures. After n attempts,
        fails gracefully.

        Override this method in your Trainer sub-classes if you would like to
        handle worker failures yourself.
        Otherwise, override only `training_step()` to implement the core
        algorithm logic.

        Returns:
            The results dict with stats/infos on sampling, training,
            and - if required - evaluation.
        """
        # Do we have to run `self.evaluate()` this iteration?
        # `self.iteration` gets incremented after this function returns,
        # meaning that e. g. the first time this function is called,
        # self.iteration will be 0.
        evaluate_this_iter = (
            self.config["evaluation_interval"] is not None
            and (self.iteration + 1) % self.config["evaluation_interval"] == 0
        )

        # Results dict for training (and if appolicable: evaluation).
        results: ResultDict = {}

        local_worker = (
            self.workers.local_worker()
            if hasattr(self.workers, "local_worker")
            else None
        )

        # Parallel eval + training: Kick off evaluation-loop and parallel train() call.
        if evaluate_this_iter and self.config["evaluation_parallel_to_training"]:
            (
                results,
                train_iter_ctx,
            ) = self._run_one_training_iteration_and_evaluation_in_parallel()
        # - No evaluation necessary, just run the next training iteration.
        # - We have to evaluate in this training iteration, but no parallelism ->
        #   evaluate after the training iteration is entirely done.
        else:
            results, train_iter_ctx = self._run_one_training_iteration()

        # Sequential: Train (already done above), then evaluate.
        if evaluate_this_iter and not self.config["evaluation_parallel_to_training"]:
            results.update(self._run_one_evaluation(train_future=None))

        # Attach latest available evaluation results to train results,
        # if necessary.
        if not evaluate_this_iter and self.config["always_attach_evaluation_results"]:
            assert isinstance(
                self.evaluation_metrics, dict
            ), "Trainer.evaluate() needs to return a dict."
            results.update(self.evaluation_metrics)

        if hasattr(self, "workers") and isinstance(self.workers, WorkerSet):
            # Sync filters on workers.
            self._sync_filters_if_needed(
                from_worker=self.workers.local_worker(),
                workers=self.workers,
                timeout_seconds=self.config[
                    "sync_filters_on_rollout_workers_timeout_s"
                ],
            )
            # Collect worker metrics and add combine them with `results`.
            if self.config["_disable_execution_plan_api"]:
                episodes_this_iter, self._episodes_to_be_collected = collect_episodes(
                    local_worker,
                    self._remote_workers_for_metrics,
                    self._episodes_to_be_collected,
                    timeout_seconds=self.config["metrics_episode_collection_timeout_s"],
                )
                results = self._compile_iteration_results(
                    episodes_this_iter=episodes_this_iter,
                    step_ctx=train_iter_ctx,
                    iteration_results=results,
                )

        # Check `env_task_fn` for possible update of the env's task.
        if self.config["env_task_fn"] is not None:
            if not callable(self.config["env_task_fn"]):
                raise ValueError(
                    "`env_task_fn` must be None or a callable taking "
                    "[train_results, env, env_ctx] as args!"
                )

            def fn(env, env_context, task_fn):
                new_task = task_fn(results, env, env_context)
                cur_task = env.get_task()
                if cur_task != new_task:
                    env.set_task(new_task)

            fn = functools.partial(fn, task_fn=self.config["env_task_fn"])
            self.workers.foreach_env_with_context(fn)

        return results

    @PublicAPI
    def evaluate(
        self,
        duration_fn: Optional[Callable[[int], int]] = None,
    ) -> dict:
        """Evaluates current policy under `evaluation_config` settings.

        Note that this default implementation does not do anything beyond
        merging evaluation_config with the normal trainer config.

        Args:
            duration_fn: An optional callable taking the already run
                num episodes as only arg and returning the number of
                episodes left to run. It's used to find out whether
                evaluation should continue.
        """
        # Call the `_before_evaluate` hook.
        self._before_evaluate()

        # Sync weights to the evaluation WorkerSet.
        if self.evaluation_workers is not None:
            self.evaluation_workers.sync_weights(
                from_worker=self.workers.local_worker()
            )
            self._sync_filters_if_needed(
                from_worker=self.workers.local_worker(),
                workers=self.evaluation_workers,
                timeout_seconds=self.config[
                    "sync_filters_on_rollout_workers_timeout_s"
                ],
            )

        if self.config["custom_eval_function"]:
            logger.info(
                "Running custom eval function {}".format(
                    self.config["custom_eval_function"]
                )
            )
            metrics = self.config["custom_eval_function"](self, self.evaluation_workers)
            if not metrics or not isinstance(metrics, dict):
                raise ValueError(
                    "Custom eval function must return "
                    "dict of metrics, got {}.".format(metrics)
                )
        else:
            if (
                self.evaluation_workers is None
                and self.workers.local_worker().input_reader is None
            ):
                raise ValueError(
                    "Cannot evaluate w/o an evaluation worker set in "
                    "the Trainer or w/o an env on the local worker!\n"
                    "Try one of the following:\n1) Set "
                    "`evaluation_interval` >= 0 to force creating a "
                    "separate evaluation worker set.\n2) Set "
                    "`create_env_on_driver=True` to force the local "
                    "(non-eval) worker to have an environment to "
                    "evaluate on."
                )

            # How many episodes/timesteps do we need to run?
            # In "auto" mode (only for parallel eval + training): Run as long
            # as training lasts.
            unit = self.config["evaluation_duration_unit"]
            eval_cfg = self.config["evaluation_config"]
            rollout = eval_cfg["rollout_fragment_length"]
            num_envs = eval_cfg["num_envs_per_worker"]
            auto = self.config["evaluation_duration"] == "auto"
            duration = (
                self.config["evaluation_duration"]
                if not auto
                else (self.config["evaluation_num_workers"] or 1)
                * (1 if unit == "episodes" else rollout)
            )
            agent_steps_this_iter = 0
            env_steps_this_iter = 0

            # Default done-function returns True, whenever num episodes
            # have been completed.
            if duration_fn is None:

                def duration_fn(num_units_done):
                    return duration - num_units_done

            logger.info(f"Evaluating current policy for {duration} {unit}.")

            metrics = None
            all_batches = []
            # No evaluation worker set ->
            # Do evaluation using the local worker. Expect error due to the
            # local worker not having an env.
            if self.evaluation_workers is None:
                # If unit=episodes -> Run n times `sample()` (each sample
                # produces exactly 1 episode).
                # If unit=ts -> Run 1 `sample()` b/c the
                # `rollout_fragment_length` is exactly the desired ts.
                iters = duration if unit == "episodes" else 1
                for _ in range(iters):
                    batch = self.workers.local_worker().sample()
                    agent_steps_this_iter += batch.agent_steps()
                    env_steps_this_iter += batch.env_steps()
                    if self.reward_estimators:
                        all_batches.append(batch)
                metrics = collect_metrics(
                    self.workers.local_worker(),
                    keep_custom_metrics=eval_cfg["keep_per_episode_custom_metrics"],
                    timeout_seconds=eval_cfg["metrics_episode_collection_timeout_s"],
                )

            # Evaluation worker set only has local worker.
            elif self.config["evaluation_num_workers"] == 0:
                # If unit=episodes -> Run n times `sample()` (each sample
                # produces exactly 1 episode).
                # If unit=ts -> Run 1 `sample()` b/c the
                # `rollout_fragment_length` is exactly the desired ts.
                iters = duration if unit == "episodes" else 1
                for _ in range(iters):
                    batch = self.evaluation_workers.local_worker().sample()
                    agent_steps_this_iter += batch.agent_steps()
                    env_steps_this_iter += batch.env_steps()
                    if self.reward_estimators:
                        all_batches.append(batch)

            # Evaluation worker set has n remote workers.
            else:
                # How many episodes have we run (across all eval workers)?
                num_units_done = 0
                _round = 0
                while True:
                    units_left_to_do = duration_fn(num_units_done)
                    if units_left_to_do <= 0:
                        break

                    _round += 1
                    try:
                        batches = ray.get(
                            [
                                w.sample.remote()
                                for i, w in enumerate(
                                    self.evaluation_workers.remote_workers()
                                )
                                if i * (1 if unit == "episodes" else rollout * num_envs)
                                < units_left_to_do
                            ],
                            timeout=self.config["evaluation_sample_timeout_s"],
                        )
                    except GetTimeoutError:
                        logger.warning(
                            "Calling `sample()` on your remote evaluation worker(s) "
                            "resulted in a timeout (after the configured "
                            f"{self.config['evaluation_sample_timeout_s']} seconds)! "
                            "Try to set `evaluation_sample_timeout_s` in your config"
                            " to a larger value."
                            + (
                                " If your episodes don't terminate easily, you may "
                                "also want to set `evaluation_duration_unit` to "
                                "'timesteps' (instead of 'episodes')."
                                if unit == "episodes"
                                else ""
                            )
                        )
                        break

                    _agent_steps = sum(b.agent_steps() for b in batches)
                    _env_steps = sum(b.env_steps() for b in batches)
                    # 1 episode per returned batch.
                    if unit == "episodes":
                        num_units_done += len(batches)
                        # Make sure all batches are exactly one episode.
                        for ma_batch in batches:
                            ma_batch = ma_batch.as_multi_agent()
                            for batch in ma_batch.policy_batches.values():
                                assert np.sum(batch[SampleBatch.DONES])
                    # n timesteps per returned batch.
                    else:
                        num_units_done += (
                            _agent_steps if self._by_agent_steps else _env_steps
                        )
                    if self.reward_estimators:
                        all_batches.extend(batches)

                    agent_steps_this_iter += _agent_steps
                    env_steps_this_iter += _env_steps

                    logger.info(
                        f"Ran round {_round} of parallel evaluation "
                        f"({num_units_done}/{duration if not auto else '?'} "
                        f"{unit} done)"
                    )

            if metrics is None:
                metrics = collect_metrics(
                    self.evaluation_workers.local_worker(),
                    self.evaluation_workers.remote_workers(),
                    keep_custom_metrics=self.config["keep_per_episode_custom_metrics"],
                    timeout_seconds=eval_cfg["metrics_episode_collection_timeout_s"],
                )
            metrics[NUM_AGENT_STEPS_SAMPLED_THIS_ITER] = agent_steps_this_iter
            metrics[NUM_ENV_STEPS_SAMPLED_THIS_ITER] = env_steps_this_iter
            # TODO: Remove this key at some point. Here for backward compatibility.
            metrics["timesteps_this_iter"] = env_steps_this_iter

            if self.reward_estimators:
                # Compute off-policy estimates
                metrics["off_policy_estimator"] = {}
                total_batch = concat_samples(all_batches)
                for name, estimator in self.reward_estimators.items():
                    estimates = estimator.estimate(total_batch)
                    metrics["off_policy_estimator"][name] = estimates

        # Evaluation does not run for every step.
        # Save evaluation metrics on trainer, so it can be attached to
        # subsequent step results as latest evaluation result.
        self.evaluation_metrics = {"evaluation": metrics}

        # Also return the results here for convenience.
        return self.evaluation_metrics

    @ExperimentalAPI
    def _evaluate_async(
        self,
        duration_fn: Optional[Callable[[int], int]] = None,
    ) -> dict:
        """Evaluates current policy under `evaluation_config` settings.

        Note that this default implementation does not do anything beyond
        merging evaluation_config with the normal trainer config.

        Args:
            duration_fn: An optional callable taking the already run
                num episodes as only arg and returning the number of
                episodes left to run. It's used to find out whether
                evaluation should continue.
        """
        # How many episodes/timesteps do we need to run?
        # In "auto" mode (only for parallel eval + training): Run as long
        # as training lasts.
        unit = self.config["evaluation_duration_unit"]
        eval_cfg = self.config["evaluation_config"]
        rollout = eval_cfg["rollout_fragment_length"]
        num_envs = eval_cfg["num_envs_per_worker"]
        auto = self.config["evaluation_duration"] == "auto"
        duration = (
            self.config["evaluation_duration"]
            if not auto
            else (self.config["evaluation_num_workers"] or 1)
            * (1 if unit == "episodes" else rollout)
        )

        # Call the `_before_evaluate` hook.
        self._before_evaluate()

        # Put weights only once into object store and use same object
        # ref to synch to all workers.
        self._evaluation_weights_seq_number += 1
        weights_ref = ray.put(self.workers.local_worker().get_weights())
        # TODO(Jun): Make sure this cannot block for e.g. 1h. Implement solution via
        #  connectors.
        self._sync_filters_if_needed(
            from_worker=self.workers.local_worker(),
            workers=self.evaluation_workers,
            timeout_seconds=eval_cfg.get("sync_filters_on_rollout_workers_timeout_s"),
        )

        if self.config["custom_eval_function"]:
            raise ValueError(
                "`custom_eval_function` not supported in combination "
                "with `enable_async_evaluation=True` config setting!"
            )
        if self.evaluation_workers is None and (
            self.workers.local_worker().input_reader is None
            or self.config["evaluation_num_workers"] == 0
        ):
            raise ValueError(
                "Evaluation w/o eval workers (calling Algorithm.evaluate() w/o "
                "evaluation specifically set up) OR evaluation without input reader "
                "OR evaluation with only a local evaluation worker "
                "(`evaluation_num_workers=0`) not supported in combination "
                "with `enable_async_evaluation=True` config setting!"
            )

        agent_steps_this_iter = 0
        env_steps_this_iter = 0

        logger.info(f"Evaluating current policy for {duration} {unit}.")

        all_batches = []

        # Default done-function returns True, whenever num episodes
        # have been completed.
        if duration_fn is None:

            def duration_fn(num_units_done):
                return duration - num_units_done

        def remote_fn(worker, w_ref, w_seq_no):
            # Pass in seq-no so that eval workers may ignore this call if no update has
            # happened since the last call to `remote_fn` (sample).
            worker.set_weights(weights=w_ref, weights_seq_no=w_seq_no)
            batch = worker.sample()
            metrics = worker.get_metrics()
            return batch, metrics, w_seq_no

        rollout_metrics = []

        # How many episodes have we run (across all eval workers)?
        num_units_done = 0
        _round = 0
        errors = []

        while len(self._evaluation_async_req_manager.workers) > 0:
            units_left_to_do = duration_fn(num_units_done)
            if units_left_to_do <= 0:
                break

            _round += 1
            # Use the AsyncRequestsManager to get ready evaluation results and
            # metrics.
            self._evaluation_async_req_manager.call_on_all_available(
                remote_fn=remote_fn,
                fn_args=[weights_ref, self._evaluation_weights_seq_number],
            )
            ready_requests = self._evaluation_async_req_manager.get_ready()

            batches = []
            i = 0
            for actor, requests in ready_requests.items():
                for req in requests:
                    try:
                        batch, metrics, seq_no = ray.get(req)
                        # Ignore results, if the weights seq-number does not match (is
                        # from a previous evaluation step) OR if we have already reached
                        # the configured duration (e.g. number of episodes to evaluate
                        # for).
                        if seq_no == self._evaluation_weights_seq_number and (
                            i * (1 if unit == "episodes" else rollout * num_envs)
                            < units_left_to_do
                        ):
                            batches.append(batch)
                            rollout_metrics.extend(metrics)
                    except RayError as e:
                        errors.append(e)
                        self._evaluation_async_req_manager.remove_workers(actor)

                    i += 1

            _agent_steps = sum(b.agent_steps() for b in batches)
            _env_steps = sum(b.env_steps() for b in batches)

            # 1 episode per returned batch.
            if unit == "episodes":
                num_units_done += len(batches)
                # Make sure all batches are exactly one episode.
                for ma_batch in batches:
                    ma_batch = ma_batch.as_multi_agent()
                    for batch in ma_batch.policy_batches.values():
                        assert np.sum(batch[SampleBatch.DONES])
            # n timesteps per returned batch.
            else:
                num_units_done += _agent_steps if self._by_agent_steps else _env_steps

            if self.reward_estimators:
                all_batches.extend(batches)

            agent_steps_this_iter += _agent_steps
            env_steps_this_iter += _env_steps

            logger.info(
                f"Ran round {_round} of parallel evaluation "
                f"({num_units_done}/{duration if not auto else '?'} "
                f"{unit} done)"
            )

        num_recreated_workers = 0
        if errors:
            num_recreated_workers = self.try_recover_from_step_attempt(
                error=errors[0],
                worker_set=self.evaluation_workers,
                ignore=eval_cfg.get("ignore_worker_failures"),
                recreate=eval_cfg.get("recreate_failed_workers"),
            )

        metrics = summarize_episodes(
            rollout_metrics,
            keep_custom_metrics=eval_cfg["keep_per_episode_custom_metrics"],
        )

        metrics["num_recreated_workers"] = num_recreated_workers

        metrics[NUM_AGENT_STEPS_SAMPLED_THIS_ITER] = agent_steps_this_iter
        metrics[NUM_ENV_STEPS_SAMPLED_THIS_ITER] = env_steps_this_iter
        # TODO: Remove this key at some point. Here for backward compatibility.
        metrics["timesteps_this_iter"] = env_steps_this_iter

        if self.reward_estimators:
            # Compute off-policy estimates
            metrics["off_policy_estimator"] = {}
            total_batch = concat_samples(all_batches)
            for name, estimator in self.reward_estimators.items():
                estimates = estimator.estimate(total_batch)
                metrics["off_policy_estimator"][name] = estimates

        # Evaluation does not run for every step.
        # Save evaluation metrics on trainer, so it can be attached to
        # subsequent step results as latest evaluation result.
        self.evaluation_metrics = {"evaluation": metrics}

        # Return evaluation results.
        return self.evaluation_metrics

    @OverrideToImplementCustomLogic
    @DeveloperAPI
    def training_step(self) -> ResultDict:
        """Default single iteration logic of an algorithm.

        - Collect on-policy samples (SampleBatches) in parallel using the
          Trainer's RolloutWorkers (@ray.remote).
        - Concatenate collected SampleBatches into one train batch.
        - Note that we may have more than one policy in the multi-agent case:
          Call the different policies' `learn_on_batch` (simple optimizer) OR
          `load_batch_into_buffer` + `learn_on_loaded_batch` (multi-GPU
          optimizer) methods to calculate loss and update the model(s).
        - Return all collected metrics for the iteration.

        Returns:
            The results dict from executing the training iteration.
        """
        # Collect SampleBatches from sample workers until we have a full batch.
        if self._by_agent_steps:
            train_batch = synchronous_parallel_sample(
                worker_set=self.workers, max_agent_steps=self.config["train_batch_size"]
            )
        else:
            train_batch = synchronous_parallel_sample(
                worker_set=self.workers, max_env_steps=self.config["train_batch_size"]
            )
        train_batch = train_batch.as_multi_agent()
        self._counters[NUM_AGENT_STEPS_SAMPLED] += train_batch.agent_steps()
        self._counters[NUM_ENV_STEPS_SAMPLED] += train_batch.env_steps()

        # Use simple optimizer (only for multi-agent or tf-eager; all other
        # cases should use the multi-GPU optimizer, even if only using 1 GPU).
        # TODO: (sven) rename MultiGPUOptimizer into something more
        #  meaningful.
        if self.config.get("simple_optimizer") is True:
            train_results = train_one_step(self, train_batch)
        else:
            train_results = multi_gpu_train_one_step(self, train_batch)

        # Update weights and global_vars - after learning on the local worker - on all
        # remote workers.
        global_vars = {
            "timestep": self._counters[NUM_ENV_STEPS_SAMPLED],
        }
        with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
            self.workers.sync_weights(global_vars=global_vars)

        return train_results

    @staticmethod
    def execution_plan(workers, config, **kwargs):
        raise NotImplementedError(
            "It is not longer recommended to use Trainer's `execution_plan` method/API."
            " Set `_disable_execution_plan_api=True` in your config and override the "
            "`Trainer.training_step()` method with your algo's custom "
            "execution logic."
        )

    @PublicAPI
    def compute_single_action(
        self,
        observation: Optional[TensorStructType] = None,
        state: Optional[List[TensorStructType]] = None,
        *,
        prev_action: Optional[TensorStructType] = None,
        prev_reward: Optional[float] = None,
        info: Optional[EnvInfoDict] = None,
        input_dict: Optional[SampleBatch] = None,
        policy_id: PolicyID = DEFAULT_POLICY_ID,
        full_fetch: bool = False,
        explore: Optional[bool] = None,
        timestep: Optional[int] = None,
        episode: Optional[Episode] = None,
        unsquash_action: Optional[bool] = None,
        clip_action: Optional[bool] = None,
        # Deprecated args.
        unsquash_actions=DEPRECATED_VALUE,
        clip_actions=DEPRECATED_VALUE,
        # Kwargs placeholder for future compatibility.
        **kwargs,
    ) -> Union[
        TensorStructType,
        Tuple[TensorStructType, List[TensorType], Dict[str, TensorType]],
    ]:
        """Computes an action for the specified policy on the local worker.

        Note that you can also access the policy object through
        self.get_policy(policy_id) and call compute_single_action() on it
        directly.

        Args:
            observation: Single (unbatched) observation from the
                environment.
            state: List of all RNN hidden (single, unbatched) state tensors.
            prev_action: Single (unbatched) previous action value.
            prev_reward: Single (unbatched) previous reward value.
            info: Env info dict, if any.
            input_dict: An optional SampleBatch that holds all the values
                for: obs, state, prev_action, and prev_reward, plus maybe
                custom defined views of the current env trajectory. Note
                that only one of `obs` or `input_dict` must be non-None.
            policy_id: Policy to query (only applies to multi-agent).
                Default: "default_policy".
            full_fetch: Whether to return extra action fetch results.
                This is always set to True if `state` is specified.
            explore: Whether to apply exploration to the action.
                Default: None -> use self.config["explore"].
            timestep: The current (sampling) time step.
            episode: This provides access to all of the internal episodes'
                state, which may be useful for model-based or multi-agent
                algorithms.
            unsquash_action: Should actions be unsquashed according to the
                env's/Policy's action space? If None, use the value of
                self.config["normalize_actions"].
            clip_action: Should actions be clipped according to the
                env's/Policy's action space? If None, use the value of
                self.config["clip_actions"].

        Keyword Args:
            kwargs: forward compatibility placeholder

        Returns:
            The computed action if full_fetch=False, or a tuple of a) the
            full output of policy.compute_actions() if full_fetch=True
            or we have an RNN-based Policy.

        Raises:
            KeyError: If the `policy_id` cannot be found in this Trainer's
                local worker.
        """
        if clip_actions != DEPRECATED_VALUE:
            deprecation_warning(
                old="Trainer.compute_single_action(`clip_actions`=...)",
                new="Trainer.compute_single_action(`clip_action`=...)",
                error=False,
            )
            clip_action = clip_actions
        if unsquash_actions != DEPRECATED_VALUE:
            deprecation_warning(
                old="Trainer.compute_single_action(`unsquash_actions`=...)",
                new="Trainer.compute_single_action(`unsquash_action`=...)",
                error=False,
            )
            unsquash_action = unsquash_actions

        # `unsquash_action` is None: Use value of config['normalize_actions'].
        if unsquash_action is None:
            unsquash_action = self.config["normalize_actions"]
        # `clip_action` is None: Use value of config['clip_actions'].
        elif clip_action is None:
            clip_action = self.config["clip_actions"]

        # User provided an input-dict: Assert that `obs`, `prev_a|r`, `state`
        # are all None.
        err_msg = (
            "Provide either `input_dict` OR [`observation`, ...] as "
            "args to Trainer.compute_single_action!"
        )
        if input_dict is not None:
            assert (
                observation is None
                and prev_action is None
                and prev_reward is None
                and state is None
            ), err_msg
            observation = input_dict[SampleBatch.OBS]
        else:
            assert observation is not None, err_msg

        # Get the policy to compute the action for (in the multi-agent case,
        # Trainer may hold >1 policies).
        policy = self.get_policy(policy_id)
        if policy is None:
            raise KeyError(
                f"PolicyID '{policy_id}' not found in PolicyMap of the "
                f"Trainer's local worker!"
            )
        local_worker = self.workers.local_worker()

        # Check the preprocessor and preprocess, if necessary.
        pp = local_worker.preprocessors[policy_id]
        if pp and type(pp).__name__ != "NoPreprocessor":
            observation = pp.transform(observation)
        observation = local_worker.filters[policy_id](observation, update=False)

        # Input-dict.
        if input_dict is not None:
            input_dict[SampleBatch.OBS] = observation
            action, state, extra = policy.compute_single_action(
                input_dict=input_dict,
                explore=explore,
                timestep=timestep,
                episode=episode,
            )
        # Individual args.
        else:
            action, state, extra = policy.compute_single_action(
                obs=observation,
                state=state,
                prev_action=prev_action,
                prev_reward=prev_reward,
                info=info,
                explore=explore,
                timestep=timestep,
                episode=episode,
            )

        # If we work in normalized action space (normalize_actions=True),
        # we re-translate here into the env's action space.
        if unsquash_action:
            action = space_utils.unsquash_action(action, policy.action_space_struct)
        # Clip, according to env's action space.
        elif clip_action:
            action = space_utils.clip_action(action, policy.action_space_struct)

        # Return 3-Tuple: Action, states, and extra-action fetches.
        if state or full_fetch:
            return action, state, extra
        # Ensure backward compatibility.
        else:
            return action

    @PublicAPI
    def compute_actions(
        self,
        observations: TensorStructType,
        state: Optional[List[TensorStructType]] = None,
        *,
        prev_action: Optional[TensorStructType] = None,
        prev_reward: Optional[TensorStructType] = None,
        info: Optional[EnvInfoDict] = None,
        policy_id: PolicyID = DEFAULT_POLICY_ID,
        full_fetch: bool = False,
        explore: Optional[bool] = None,
        timestep: Optional[int] = None,
        episodes: Optional[List[Episode]] = None,
        unsquash_actions: Optional[bool] = None,
        clip_actions: Optional[bool] = None,
        # Deprecated.
        normalize_actions=None,
        **kwargs,
    ):
        """Computes an action for the specified policy on the local Worker.

        Note that you can also access the policy object through
        self.get_policy(policy_id) and call compute_actions() on it directly.

        Args:
            observation: Observation from the environment.
            state: RNN hidden state, if any. If state is not None,
                then all of compute_single_action(...) is returned
                (computed action, rnn state(s), logits dictionary).
                Otherwise compute_single_action(...)[0] is returned
                (computed action).
            prev_action: Previous action value, if any.
            prev_reward: Previous reward, if any.
            info: Env info dict, if any.
            policy_id: Policy to query (only applies to multi-agent).
            full_fetch: Whether to return extra action fetch results.
                This is always set to True if RNN state is specified.
            explore: Whether to pick an exploitation or exploration
                action (default: None -> use self.config["explore"]).
            timestep: The current (sampling) time step.
            episodes: This provides access to all of the internal episodes'
                state, which may be useful for model-based or multi-agent
                algorithms.
            unsquash_actions: Should actions be unsquashed according
                to the env's/Policy's action space? If None, use
                self.config["normalize_actions"].
            clip_actions: Should actions be clipped according to the
                env's/Policy's action space? If None, use
                self.config["clip_actions"].

        Keyword Args:
            kwargs: forward compatibility placeholder

        Returns:
            The computed action if full_fetch=False, or a tuple consisting of
            the full output of policy.compute_actions_from_input_dict() if
            full_fetch=True or we have an RNN-based Policy.
        """
        if normalize_actions is not None:
            deprecation_warning(
                old="Trainer.compute_actions(`normalize_actions`=...)",
                new="Trainer.compute_actions(`unsquash_actions`=...)",
                error=False,
            )
            unsquash_actions = normalize_actions

        # `unsquash_actions` is None: Use value of config['normalize_actions'].
        if unsquash_actions is None:
            unsquash_actions = self.config["normalize_actions"]
        # `clip_actions` is None: Use value of config['clip_actions'].
        elif clip_actions is None:
            clip_actions = self.config["clip_actions"]

        # Preprocess obs and states.
        state_defined = state is not None
        policy = self.get_policy(policy_id)
        filtered_obs, filtered_state = [], []
        for agent_id, ob in observations.items():
            worker = self.workers.local_worker()
            preprocessed = worker.preprocessors[policy_id].transform(ob)
            filtered = worker.filters[policy_id](preprocessed, update=False)
            filtered_obs.append(filtered)
            if state is None:
                continue
            elif agent_id in state:
                filtered_state.append(state[agent_id])
            else:
                filtered_state.append(policy.get_initial_state())

        # Batch obs and states
        obs_batch = np.stack(filtered_obs)
        if state is None:
            state = []
        else:
            state = list(zip(*filtered_state))
            state = [np.stack(s) for s in state]

        input_dict = {SampleBatch.OBS: obs_batch}

        # prev_action and prev_reward can be None, np.ndarray, or tensor-like structure.
        # Explicitly check for None here to avoid the error message "The truth value of
        # an array with more than one element is ambiguous.", when np arrays are passed
        # as arguments.
        if prev_action is not None:
            input_dict[SampleBatch.PREV_ACTIONS] = prev_action
        if prev_reward is not None:
            input_dict[SampleBatch.PREV_REWARDS] = prev_reward
        if info:
            input_dict[SampleBatch.INFOS] = info
        for i, s in enumerate(state):
            input_dict[f"state_in_{i}"] = s

        # Batch compute actions
        actions, states, infos = policy.compute_actions_from_input_dict(
            input_dict=input_dict,
            explore=explore,
            timestep=timestep,
            episodes=episodes,
        )

        # Unbatch actions for the environment into a multi-agent dict.
        single_actions = space_utils.unbatch(actions)
        actions = {}
        for key, a in zip(observations, single_actions):
            # If we work in normalized action space (normalize_actions=True),
            # we re-translate here into the env's action space.
            if unsquash_actions:
                a = space_utils.unsquash_action(a, policy.action_space_struct)
            # Clip, according to env's action space.
            elif clip_actions:
                a = space_utils.clip_action(a, policy.action_space_struct)
            actions[key] = a

        # Unbatch states into a multi-agent dict.
        unbatched_states = {}
        for idx, agent_id in enumerate(observations):
            unbatched_states[agent_id] = [s[idx] for s in states]

        # Return only actions or full tuple
        if state_defined or full_fetch:
            return actions, unbatched_states, infos
        else:
            return actions

    @PublicAPI
    def get_policy(self, policy_id: PolicyID = DEFAULT_POLICY_ID) -> Policy:
        """Return policy for the specified id, or None.

        Args:
            policy_id: ID of the policy to return.
        """
        return self.workers.local_worker().get_policy(policy_id)

    @PublicAPI
    def get_weights(self, policies: Optional[List[PolicyID]] = None) -> dict:
        """Return a dictionary of policy ids to weights.

        Args:
            policies: Optional list of policies to return weights for,
                or None for all policies.
        """
        return self.workers.local_worker().get_weights(policies)

    @PublicAPI
    def set_weights(self, weights: Dict[PolicyID, dict]):
        """Set policy weights by policy id.

        Args:
            weights: Map of policy ids to weights to set.
        """
        self.workers.local_worker().set_weights(weights)

    @PublicAPI
    def add_policy(
        self,
        policy_id: PolicyID,
        policy_cls: Type[Policy],
        *,
        observation_space: Optional[gym.spaces.Space] = None,
        action_space: Optional[gym.spaces.Space] = None,
        config: Optional[PartialAlgorithmConfigDict] = None,
        policy_state: Optional[PolicyState] = None,
        policy_mapping_fn: Optional[Callable[[AgentID, EpisodeID], PolicyID]] = None,
        policies_to_train: Optional[
            Union[
                Container[PolicyID],
                Callable[[PolicyID, Optional[SampleBatchType]], bool],
            ]
        ] = None,
        evaluation_workers: bool = True,
        workers: Optional[List[Union[RolloutWorker, ActorHandle]]] = None,
    ) -> Policy:
        """Adds a new policy to this Trainer.

        Args:
            policy_id: ID of the policy to add.
            policy_cls: The Policy class to use for
                constructing the new Policy.
            observation_space: The observation space of the policy to add.
                If None, try to infer this space from the environment.
            action_space: The action space of the policy to add.
                If None, try to infer this space from the environment.
            config: The config overrides for the policy to add.
            policy_state: Optional state dict to apply to the new
                policy instance, right after its construction.
            policy_mapping_fn: An optional (updated) policy mapping function
                to use from here on. Note that already ongoing episodes will
                not change their mapping but will use the old mapping till
                the end of the episode.
            policies_to_train: An optional list of policy IDs to be trained
                or a callable taking PolicyID and SampleBatchType and
                returning a bool (trainable or not?).
                If None, will keep the existing setup in place. Policies,
                whose IDs are not in the list (or for which the callable
                returns False) will not be updated.
            evaluation_workers: Whether to add the new policy also
                to the evaluation WorkerSet.
            workers: A list of RolloutWorker/ActorHandles (remote
                RolloutWorkers) to add this policy to. If defined, will only
                add the given policy to these workers.

        Returns:
            The newly added policy (the copy that got added to the local
            worker).
        """

        kwargs = dict(
            policy_id=policy_id,
            policy_cls=policy_cls,
            observation_space=observation_space,
            action_space=action_space,
            config=config,
            policy_state=policy_state,
            policy_mapping_fn=policy_mapping_fn,
            policies_to_train=list(policies_to_train) if policies_to_train else None,
        )

        def fn(worker: RolloutWorker):
            # `foreach_worker` function: Adds the policy the the worker (and
            # maybe changes its policy_mapping_fn - if provided here).
            worker.add_policy(**kwargs)

        if workers is not None:
            ray_gets = []
            for worker in workers:
                if isinstance(worker, ActorHandle):
                    ray_gets.append(worker.add_policy.remote(**kwargs))
                else:
                    fn(worker)
            ray.get(ray_gets)
        else:
            # Run foreach_worker fn on all workers.
            self.workers.foreach_worker(fn)

        # Update evaluation workers, if necessary.
        if evaluation_workers and self.evaluation_workers is not None:
            self.evaluation_workers.foreach_worker(fn)

        # Return newly added policy (from the local rollout worker).
        return self.get_policy(policy_id)

    @PublicAPI
    def remove_policy(
        self,
        policy_id: PolicyID = DEFAULT_POLICY_ID,
        *,
        policy_mapping_fn: Optional[Callable[[AgentID], PolicyID]] = None,
        policies_to_train: Optional[
            Union[Set[PolicyID], Callable[[PolicyID, Optional[SampleBatchType]], bool]]
        ] = None,
        evaluation_workers: bool = True,
    ) -> None:
        """Removes a new policy from this Trainer.

        Args:
            policy_id: ID of the policy to be removed.
            policy_mapping_fn: An optional (updated) policy mapping function
                to use from here on. Note that already ongoing episodes will
                not change their mapping but will use the old mapping till
                the end of the episode.
            policies_to_train: An optional list of policy IDs to be trained
                or a callable taking PolicyID and SampleBatchType and
                returning a bool (trainable or not?).
                If None, will keep the existing setup in place. Policies,
                whose IDs are not in the list (or for which the callable
                returns False) will not be updated.
            evaluation_workers: Whether to also remove the policy from the
                evaluation WorkerSet.
        """

        def fn(worker):
            worker.remove_policy(
                policy_id=policy_id,
                policy_mapping_fn=policy_mapping_fn,
                policies_to_train=policies_to_train,
            )

        self.workers.foreach_worker(fn)
        if evaluation_workers and self.evaluation_workers is not None:
            self.evaluation_workers.foreach_worker(fn)

    @DeveloperAPI
    def export_policy_model(
        self,
        export_dir: str,
        policy_id: PolicyID = DEFAULT_POLICY_ID,
        onnx: Optional[int] = None,
    ) -> None:
        """Exports policy model with given policy_id to a local directory.

        Args:
            export_dir: Writable local directory.
            policy_id: Optional policy id to export.
            onnx: If given, will export model in ONNX format. The
                value of this parameter set the ONNX OpSet version to use.
                If None, the output format will be DL framework specific.

        Example:
            >>> from ray.rllib.algorithms.ppo import PPO
            >>> # Use a Trainer from RLlib or define your own.
            >>> trainer = PPO(...) # doctest: +SKIP
            >>> for _ in range(10): # doctest: +SKIP
            >>>     trainer.train() # doctest: +SKIP
            >>> trainer.export_policy_model("/tmp/dir") # doctest: +SKIP
            >>> trainer.export_policy_model("/tmp/dir/onnx", onnx=1) # doctest: +SKIP
        """
        self.get_policy(policy_id).export_model(export_dir, onnx)

    @DeveloperAPI
    def export_policy_checkpoint(
        self,
        export_dir: str,
        filename_prefix: str = "model",
        policy_id: PolicyID = DEFAULT_POLICY_ID,
    ) -> None:
        """Exports policy model checkpoint to a local directory.

        Args:
            export_dir: Writable local directory.
            filename_prefix: file name prefix of checkpoint files.
            policy_id: Optional policy id to export.

        Example:
            >>> from ray.rllib.algorithms.ppo import PPO
            >>> # Use a Trainer from RLlib or define your own.
            >>> trainer = PPO(...) # doctest: +SKIP
            >>> for _ in range(10): # doctest: +SKIP
            >>>     trainer.train() # doctest: +SKIP
            >>> trainer.export_policy_checkpoint("/tmp/export_dir") # doctest: +SKIP
        """
        self.get_policy(policy_id).export_checkpoint(export_dir, filename_prefix)

    @DeveloperAPI
    def import_policy_model_from_h5(
        self,
        import_file: str,
        policy_id: PolicyID = DEFAULT_POLICY_ID,
    ) -> None:
        """Imports a policy's model with given policy_id from a local h5 file.

        Args:
            import_file: The h5 file to import from.
            policy_id: Optional policy id to import into.

        Example:
            >>> from ray.rllib.algorithms.ppo import PPO
            >>> trainer = PPO(...) # doctest: +SKIP
            >>> trainer.import_policy_model_from_h5("/tmp/weights.h5") # doctest: +SKIP
            >>> for _ in range(10): # doctest: +SKIP
            >>>     trainer.train() # doctest: +SKIP
        """
        self.get_policy(policy_id).import_model_from_h5(import_file)
        # Sync new weights to remote workers.
        self._sync_weights_to_workers(worker_set=self.workers)

    @override(Trainable)
    def save_checkpoint(self, checkpoint_dir: str) -> str:
        checkpoint_path = os.path.join(
            checkpoint_dir, "checkpoint-{}".format(self.iteration)
        )
        pickle.dump(self.__getstate__(), open(checkpoint_path, "wb"))

        return checkpoint_path

    @override(Trainable)
    def load_checkpoint(self, checkpoint_path: str) -> None:
        extra_data = pickle.load(open(checkpoint_path, "rb"))
        self.__setstate__(extra_data)

    @override(Trainable)
    def log_result(self, result: ResultDict) -> None:
        # Log after the callback is invoked, so that the user has a chance
        # to mutate the result.
        # TODO: Remove `trainer` arg at some point to fully deprecate the old signature.
        self.callbacks.on_train_result(algorithm=self, result=result, trainer=self)
        # Then log according to Trainable's logging logic.
        Trainable.log_result(self, result)

    @override(Trainable)
    def cleanup(self) -> None:
        # Stop all workers.
        if hasattr(self, "workers") and self.workers is not None:
            self.workers.stop()
        if hasattr(self, "evaluation_workers") and self.evaluation_workers is not None:
            self.evaluation_workers.stop()

    @OverrideToImplementCustomLogic
    @classmethod
    @override(Trainable)
    def default_resource_request(
        cls, config: PartialAlgorithmConfigDict
    ) -> Union[Resources, PlacementGroupFactory]:

        # Default logic for RLlib algorithms (Trainers):
        # Create one bundle per individual worker (local or remote).
        # Use `num_cpus_for_driver` and `num_gpus` for the local worker and
        # `num_cpus_per_worker` and `num_gpus_per_worker` for the remote
        # workers to determine their CPU/GPU resource needs.

        # Convenience config handles.
        cf = dict(cls.get_default_config(), **config)
        eval_cf = cf["evaluation_config"]

        local_worker = {
            "CPU": cf["num_cpus_for_driver"],
            "GPU": 0 if cf["_fake_gpus"] else cf["num_gpus"],
        }
        rollout_workers = [
            {
                "CPU": cf["num_cpus_per_worker"],
                "GPU": cf["num_gpus_per_worker"],
                **cf["custom_resources_per_worker"],
            }
            for _ in range(cf["num_workers"])
        ]

        bundles = [local_worker] + rollout_workers

        if cf["evaluation_interval"]:
            # Evaluation workers.
            # Note: The local eval worker is located on the driver CPU.
            bundles += [
                {
                    "CPU": eval_cf.get(
                        "num_cpus_per_worker", cf["num_cpus_per_worker"]
                    ),
                    "GPU": eval_cf.get(
                        "num_gpus_per_worker", cf["num_gpus_per_worker"]
                    ),
                    **eval_cf.get(
                        "custom_resources_per_worker", cf["custom_resources_per_worker"]
                    ),
                }
                for _ in range(cf["evaluation_num_workers"])
            ]

        # In case our I/O reader/writer requires conmpute resources.
        bundles += get_offline_io_resource_bundles(cf)

        # Return PlacementGroupFactory containing all needed resources
        # (already properly defined as device bundles).
        return PlacementGroupFactory(
            bundles=bundles,
            strategy=config.get("placement_strategy", "PACK"),
        )

    @DeveloperAPI
    def _before_evaluate(self):
        """Pre-evaluation callback."""
        pass

    @staticmethod
    def _get_env_id_and_creator(
        env_specifier: Union[str, EnvType, None], config: PartialAlgorithmConfigDict
    ) -> Tuple[Optional[str], EnvCreator]:
        """Returns env_id and creator callable given original env id from config.

        Args:
            env_specifier: An env class, an already tune registered env ID, a known
                gym env name, or None (if no env is used).
            config: The Trainer's (maybe partial) config dict.

        Returns:
            Tuple consisting of a) env ID string and b) env creator callable.
        """
        # Environment is specified via a string.
        if isinstance(env_specifier, str):
            # An already registered env.
            if _global_registry.contains(ENV_CREATOR, env_specifier):
                return env_specifier, _global_registry.get(ENV_CREATOR, env_specifier)

            # A class path specifier.
            elif "." in env_specifier:

                def env_creator_from_classpath(env_context):
                    try:
                        env_obj = from_config(env_specifier, env_context)
                    except ValueError:
                        raise EnvError(
                            ERR_MSG_INVALID_ENV_DESCRIPTOR.format(env_specifier)
                        )
                    return env_obj

                return env_specifier, env_creator_from_classpath
            # Try gym/PyBullet/Vizdoom.
            else:
                return env_specifier, functools.partial(
                    _gym_env_creator, env_descriptor=env_specifier
                )

        elif isinstance(env_specifier, type):
            env_id = env_specifier  # .__name__

            if config.get("remote_worker_envs"):
                # Check gym version (0.22 or higher?).
                # If > 0.21, can't perform auto-wrapping of the given class as this
                # would lead to a pickle error.
                gym_version = pkg_resources.get_distribution("gym").version
                if version.parse(gym_version) >= version.parse("0.22"):
                    raise ValueError(
                        "Cannot specify a gym.Env class via `config.env` while setting "
                        "`config.remote_worker_env=True` AND your gym version is >= "
                        "0.22! Try installing an older version of gym or set `config."
                        "remote_worker_env=False`."
                    )

                @ray.remote(num_cpus=1)
                class _wrapper(env_specifier):
                    # Add convenience `_get_spaces` and `_is_multi_agent`
                    # methods:
                    def _get_spaces(self):
                        return self.observation_space, self.action_space

                    def _is_multi_agent(self):
                        from ray.rllib.env.multi_agent_env import MultiAgentEnv

                        return isinstance(self, MultiAgentEnv)

                return env_id, lambda cfg: _wrapper.remote(cfg)
            else:
                return env_id, lambda cfg: env_specifier(cfg)

        # No env -> Env creator always returns None.
        elif env_specifier is None:
            return None, lambda env_config: None

        else:
            raise ValueError(
                "{} is an invalid env specifier. ".format(env_specifier)
                + "You can specify a custom env as either a class "
                '(e.g., YourEnvCls) or a registered env id (e.g., "your_env").'
            )

    def _sync_filters_if_needed(
        self,
        from_worker: RolloutWorker,
        workers: WorkerSet,
        timeout_seconds: Optional[float] = None,
    ):
        if (
            from_worker
            and self.config.get("observation_filter", "NoFilter") != "NoFilter"
        ):
            FilterManager.synchronize(
                from_worker.filters,
                workers.remote_workers(),
                update_remote=self.config["synchronize_filters"],
                timeout_seconds=timeout_seconds,
            )
            logger.debug("synchronized filters: {}".format(from_worker.filters))

    @DeveloperAPI
    def _sync_weights_to_workers(
        self,
        *,
        worker_set: Optional[WorkerSet] = None,
        workers: Optional[List[RolloutWorker]] = None,
    ) -> None:
        """Sync "main" weights to given WorkerSet or list of workers."""
        assert worker_set is not None
        # Broadcast the new policy weights to all evaluation workers.
        logger.info("Synchronizing weights to workers.")
        weights = ray.put(self.workers.local_worker().save())
        worker_set.foreach_worker(lambda w: w.restore(ray.get(weights)))

    @classmethod
    @override(Trainable)
    def resource_help(cls, config: AlgorithmConfigDict) -> str:
        return (
            "\n\nYou can adjust the resource requests of RLlib agents by "
            "setting `num_workers`, `num_gpus`, and other configs. See "
            "the DEFAULT_CONFIG defined by each agent for more info.\n\n"
            "The config of this agent is: {}".format(config)
        )

    @classmethod
    def merge_trainer_configs(
        cls,
        config1: AlgorithmConfigDict,
        config2: PartialAlgorithmConfigDict,
        _allow_unknown_configs: Optional[bool] = None,
    ) -> AlgorithmConfigDict:
        """Merges a complete Trainer config with a partial override dict.

        Respects nested structures within the config dicts. The values in the
        partial override dict take priority.

        Args:
            config1: The complete Trainer's dict to be merged (overridden)
                with `config2`.
            config2: The partial override config dict to merge on top of
                `config1`.
            _allow_unknown_configs: If True, keys in `config2` that don't exist
                in `config1` are allowed and will be added to the final config.

        Returns:
            The merged full trainer config dict.
        """
        config1 = copy.deepcopy(config1)
        if "callbacks" in config2 and type(config2["callbacks"]) is dict:
            legacy_callbacks_dict = config2["callbacks"]

            def make_callbacks():
                # Deprecation warning will be logged by DefaultCallbacks.
                return DefaultCallbacks(legacy_callbacks_dict=legacy_callbacks_dict)

            config2["callbacks"] = make_callbacks
        if _allow_unknown_configs is None:
            _allow_unknown_configs = cls._allow_unknown_configs
        return deep_update(
            config1,
            config2,
            _allow_unknown_configs,
            cls._allow_unknown_subkeys,
            cls._override_all_subkeys_if_type_changes,
            cls._override_all_key_list,
        )

    @staticmethod
    def validate_framework(config: PartialAlgorithmConfigDict) -> None:
        """Validates the config dictionary wrt the framework settings.

        Args:
            config: The config dictionary to be validated.

        """
        _tf1, _tf, _tfv = None, None, None
        _torch = None
        framework = config["framework"]
        tf_valid_frameworks = {"tf", "tf2", "tfe"}
        if framework not in tf_valid_frameworks and framework != "torch":
            return
        elif framework in tf_valid_frameworks:
            _tf1, _tf, _tfv = try_import_tf()
        else:
            _torch, _ = try_import_torch()

        def check_if_correct_nn_framework_installed():
            """Check if tf/torch experiment is running and tf/torch installed."""
            if framework in tf_valid_frameworks:
                if not (_tf1 or _tf):
                    raise ImportError(
                        (
                            "TensorFlow was specified as the 'framework' "
                            "inside of your config dictionary. However, there was "
                            "no installation found. You can install TensorFlow "
                            "via `pip install tensorflow`"
                        )
                    )
            elif framework == "torch":
                if not _torch:
                    raise ImportError(
                        (
                            "PyTorch was specified as the 'framework' inside "
                            "of your config dictionary. However, there was no "
                            "installation found. You can install PyTorch via "
                            "`pip install torch`"
                        )
                    )

        def resolve_tf_settings():
            """Check and resolve tf settings."""

            if _tf1 and config["framework"] in ["tf2", "tfe"]:
                if config["framework"] == "tf2" and _tfv < 2:
                    raise ValueError(
                        "You configured `framework`=tf2, but your installed "
                        "pip tf-version is < 2.0! Make sure your TensorFlow "
                        "version is >= 2.x."
                    )
                if not _tf1.executing_eagerly():
                    _tf1.enable_eager_execution()
                # Recommend setting tracing to True for speedups.
                logger.info(
                    f"Executing eagerly (framework='{config['framework']}'),"
                    f" with eager_tracing={config['eager_tracing']}. For "
                    "production workloads, make sure to set eager_tracing=True"
                    "  in order to match the speed of tf-static-graph "
                    "(framework='tf'). For debugging purposes, "
                    "`eager_tracing=False` is the best choice."
                )
            # Tf-static-graph (framework=tf): Recommend upgrading to tf2 and
            # enabling eager tracing for similar speed.
            elif _tf1 and config["framework"] == "tf":
                logger.info(
                    "Your framework setting is 'tf', meaning you are using "
                    "static-graph mode. Set framework='tf2' to enable eager "
                    "execution with tf2.x. You may also then want to set "
                    "eager_tracing=True in order to reach similar execution "
                    "speed as with static-graph mode."
                )

        check_if_correct_nn_framework_installed()
        resolve_tf_settings()

    @OverrideToImplementCustomLogic_CallToSuperRecommended
    @DeveloperAPI
    def validate_config(self, config: AlgorithmConfigDict) -> None:
        """Validates a given config dict for this Trainer.

        Users should override this method to implement custom validation
        behavior. It is recommended to call `super().validate_config()` in
        this override.

        Args:
            config: The given config dict to check.

        Raises:
            ValueError: If there is something wrong with the config.
        """
        model_config = config.get("model")
        if model_config is None:
            config["model"] = model_config = {}

        # Use DefaultCallbacks class, if callbacks is None.
        if config["callbacks"] is None:
            config["callbacks"] = DefaultCallbacks
        # Check, whether given `callbacks` is a callable.
        if not callable(config["callbacks"]):
            raise ValueError(
                "`callbacks` must be a callable method that "
                "returns a subclass of DefaultCallbacks, got "
                f"{config['callbacks']}!"
            )

        # Multi-GPU settings.
        simple_optim_setting = config.get("simple_optimizer", DEPRECATED_VALUE)
        if simple_optim_setting != DEPRECATED_VALUE:
            deprecation_warning(old="simple_optimizer", error=False)

        # Validate "multiagent" sub-dict and convert policy 4-tuples to
        # PolicySpec objects.
        policies, is_multi_agent = check_multi_agent(config)

        framework = config.get("framework")
        # Multi-GPU setting: Must use MultiGPUTrainOneStep.
        if config.get("num_gpus", 0) > 1:
            if framework in ["tfe", "tf2"]:
                raise ValueError(
                    "`num_gpus` > 1 not supported yet for "
                    "framework={}!".format(framework)
                )
            elif simple_optim_setting is True:
                raise ValueError(
                    "Cannot use `simple_optimizer` if `num_gpus` > 1! "
                    "Consider not setting `simple_optimizer` in your config."
                )
            config["simple_optimizer"] = False
        # Auto-setting: Use simple-optimizer for tf-eager or multiagent,
        # otherwise: MultiGPUTrainOneStep (if supported by the algo's execution
        # plan).
        elif simple_optim_setting == DEPRECATED_VALUE:
            # tf-eager: Must use simple optimizer.
            if framework not in ["tf", "torch"]:
                config["simple_optimizer"] = True
            # Multi-agent case: Try using MultiGPU optimizer (only
            # if all policies used are DynamicTFPolicies or TorchPolicies).
            elif is_multi_agent:
                from ray.rllib.policy.dynamic_tf_policy import DynamicTFPolicy
                from ray.rllib.policy.torch_policy import TorchPolicy

                default_policy_cls = self.get_default_policy_class(config)
                if any(
                    (p.policy_class or default_policy_cls) is None
                    or not issubclass(
                        p.policy_class or default_policy_cls,
                        (DynamicTFPolicy, TorchPolicy),
                    )
                    for p in config["multiagent"]["policies"].values()
                ):
                    config["simple_optimizer"] = True
                else:
                    config["simple_optimizer"] = False
            else:
                config["simple_optimizer"] = False

        # User manually set simple-optimizer to False -> Error if tf-eager.
        elif simple_optim_setting is False:
            if framework in ["tfe", "tf2"]:
                raise ValueError(
                    "`simple_optimizer=False` not supported for "
                    "framework={}!".format(framework)
                )

        # Check model config.
        # If no preprocessing, propagate into model's config as well
        # (so model will know, whether inputs are preprocessed or not).
        if config["_disable_preprocessor_api"] is True:
            model_config["_disable_preprocessor_api"] = True
        # If no action flattening, propagate into model's config as well
        # (so model will know, whether action inputs are already flattened or
        # not).
        if config["_disable_action_flattening"] is True:
            model_config["_disable_action_flattening"] = True

        # Prev_a/r settings.
        prev_a_r = model_config.get("lstm_use_prev_action_reward", DEPRECATED_VALUE)
        if prev_a_r != DEPRECATED_VALUE:
            deprecation_warning(
                "model.lstm_use_prev_action_reward",
                "model.lstm_use_prev_action and model.lstm_use_prev_reward",
                error=False,
            )
            model_config["lstm_use_prev_action"] = prev_a_r
            model_config["lstm_use_prev_reward"] = prev_a_r

        # Check batching/sample collection settings.
        if config["batch_mode"] not in ["truncate_episodes", "complete_episodes"]:
            raise ValueError(
                "`batch_mode` must be one of [truncate_episodes|"
                "complete_episodes]! Got {}".format(config["batch_mode"])
            )

        # Store multi-agent batch count mode.
        self._by_agent_steps = (
            self.config["multiagent"].get("count_steps_by") == "agent_steps"
        )

        # Metrics settings.
        if (
            config.get("metrics_smoothing_episodes", DEPRECATED_VALUE)
            != DEPRECATED_VALUE
        ):
            deprecation_warning(
                old="metrics_smoothing_episodes",
                new="metrics_num_episodes_for_smoothing",
                error=False,
            )
            config["metrics_num_episodes_for_smoothing"] = config[
                "metrics_smoothing_episodes"
            ]
        if config.get("min_iter_time_s", DEPRECATED_VALUE) != DEPRECATED_VALUE:
            deprecation_warning(
                old="min_iter_time_s",
                new="min_time_s_per_iteration",
                error=False,
            )
            config["min_time_s_per_iteration"] = config["min_iter_time_s"] or 0

        if config.get("min_time_s_per_reporting", DEPRECATED_VALUE) != DEPRECATED_VALUE:
            deprecation_warning(
                old="min_time_s_per_reporting",
                new="min_time_s_per_iteration",
                error=False,
            )
            config["min_time_s_per_iteration"] = config["min_time_s_per_reporting"] or 0

        if (
            config.get("min_sample_timesteps_per_reporting", DEPRECATED_VALUE)
            != DEPRECATED_VALUE
        ):
            deprecation_warning(
                old="min_sample_timesteps_per_reporting",
                new="min_sample_timesteps_per_iteration",
                error=False,
            )
            config["min_sample_timesteps_per_iteration"] = (
                config["min_sample_timesteps_per_reporting"] or 0
            )

        if (
            config.get("min_train_timesteps_per_reporting", DEPRECATED_VALUE)
            != DEPRECATED_VALUE
        ):
            deprecation_warning(
                old="min_train_timesteps_per_reporting",
                new="min_train_timesteps_per_iteration",
                error=False,
            )
            config["min_train_timesteps_per_iteration"] = (
                config["min_train_timesteps_per_reporting"] or 0
            )

        if config.get("collect_metrics_timeout", DEPRECATED_VALUE) != DEPRECATED_VALUE:
            # TODO: Warn once all algos use the `training_iteration` method.
            # deprecation_warning(
            #     old="collect_metrics_timeout",
            #     new="metrics_episode_collection_timeout_s",
            #     error=False,
            # )
            config["metrics_episode_collection_timeout_s"] = config[
                "collect_metrics_timeout"
            ]

        if config.get("timesteps_per_iteration", DEPRECATED_VALUE) != DEPRECATED_VALUE:
            deprecation_warning(
                old="timesteps_per_iteration",
                new="`min_sample_timesteps_per_iteration` OR "
                "`min_train_timesteps_per_iteration`",
                error=False,
            )
            config["min_sample_timesteps_per_iteration"] = (
                config["timesteps_per_iteration"] or 0
            )
            config["timesteps_per_iteration"] = DEPRECATED_VALUE

        # Evaluation settings.

        # Deprecated setting: `evaluation_num_episodes`.
        if config.get("evaluation_num_episodes", DEPRECATED_VALUE) != DEPRECATED_VALUE:
            deprecation_warning(
                old="evaluation_num_episodes",
                new="`evaluation_duration` and `evaluation_duration_unit=episodes`",
                error=False,
            )
            config["evaluation_duration"] = config["evaluation_num_episodes"]
            config["evaluation_duration_unit"] = "episodes"
            config["evaluation_num_episodes"] = DEPRECATED_VALUE

        # If `evaluation_num_workers` > 0, warn if `evaluation_interval` is
        # None (also set `evaluation_interval` to 1).
        if config["evaluation_num_workers"] > 0 and not config["evaluation_interval"]:
            logger.warning(
                f"You have specified {config['evaluation_num_workers']} "
                "evaluation workers, but your `evaluation_interval` is None! "
                "Therefore, evaluation will not occur automatically with each"
                " call to `Trainer.train()`. Instead, you will have to call "
                "`Trainer.evaluate()` manually in order to trigger an "
                "evaluation run."
            )
        # If `evaluation_num_workers=0` and
        # `evaluation_parallel_to_training=True`, warn that you need
        # at least one remote eval worker for parallel training and
        # evaluation, and set `evaluation_parallel_to_training` to False.
        elif config["evaluation_num_workers"] == 0 and config.get(
            "evaluation_parallel_to_training", False
        ):
            logger.warning(
                "`evaluation_parallel_to_training` can only be done if "
                "`evaluation_num_workers` > 0! Setting "
                "`evaluation_parallel_to_training` to False."
            )
            config["evaluation_parallel_to_training"] = False

        # If `evaluation_duration=auto`, error if
        # `evaluation_parallel_to_training=False`.
        if config["evaluation_duration"] == "auto":
            if not config["evaluation_parallel_to_training"]:
                raise ValueError(
                    "`evaluation_duration=auto` not supported for "
                    "`evaluation_parallel_to_training=False`!"
                )
        # Make sure, it's an int otherwise.
        elif (
            not isinstance(config["evaluation_duration"], int)
            or config["evaluation_duration"] <= 0
        ):
            raise ValueError(
                "`evaluation_duration` ({}) must be an int and "
                ">0!".format(config["evaluation_duration"])
            )

    @staticmethod
    @ExperimentalAPI
    def validate_env(env: EnvType, env_context: EnvContext) -> None:
        """Env validator function for this Trainer class.

        Override this in child classes to define custom validation
        behavior.

        Args:
            env: The (sub-)environment to validate. This is normally a
                single sub-environment (e.g. a gym.Env) within a vectorized
                setup.
            env_context: The EnvContext to configure the environment.

        Raises:
            Exception in case something is wrong with the given environment.
        """
        pass

    def try_recover_from_step_attempt(self, error, worker_set, ignore, recreate) -> int:
        """Try to identify and remove any unhealthy workers (incl. eval workers).

        This method is called after an unexpected remote error is encountered
        from a worker during the call to `self.step()`. It issues check requests to
        all current workers and removes any that respond with error. If no healthy
        workers remain, an error is raised.

        Returns:
            The number of remote workers recreated.
        """
        # @ray.remote RolloutWorker failure.
        if isinstance(error, RayError):
            # Try to recover w/o the failed worker.
            if ignore or recreate:
                logger.exception(
                    "Error in training or evaluation attempt! Trying to recover."
                )
            # Error out.
            else:
                logger.warning(
                    "Worker crashed during training or evaluation! "
                    "To try to continue without failed "
                    "worker(s), set `ignore_worker_failures=True`. "
                    "To try to recover the failed worker(s), set "
                    "`recreate_failed_workers=True`."
                )
                raise error
        # Any other exception.
        else:
            # Allow logs messages to propagate.
            time.sleep(0.5)
            raise error

        removed_workers, new_workers = [], []
        # Search for failed workers and try to recover (restart) them.
        if recreate:
            removed_workers, new_workers = worker_set.recreate_failed_workers(
                local_worker_for_synching=self.workers.local_worker()
            )
        elif ignore:
            removed_workers = worker_set.remove_failed_workers()

        # If `worker_set` is the main training WorkerSet: `self.workers`.
        if worker_set is getattr(self, "workers", None):
            # Call the `on_worker_failures` callback.
            self.on_worker_failures(removed_workers, new_workers)

            # Recreate execution_plan iterator.
            if not self.config.get("_disable_execution_plan_api") and callable(
                self.execution_plan
            ):
                logger.warning("Recreating execution plan after failure")
                self.train_exec_impl = self.execution_plan(
                    worker_set, self.config, **self._kwargs_for_execution_plan()
                )
        elif self._evaluation_async_req_manager is not None and worker_set is getattr(
            self, "evaluation_workers", None
        ):
            self._evaluation_async_req_manager.remove_workers(removed_workers)
            self._evaluation_async_req_manager.add_workers(new_workers)

        return len(new_workers)

    def on_worker_failures(
        self, removed_workers: List[ActorHandle], new_workers: List[ActorHandle]
    ):
        """Called after a worker failure is detected.

        Args:
            removed_workers: List of removed workers.
            new_workers: List of new workers.
        """
        pass

    @override(Trainable)
    def _export_model(
        self, export_formats: List[str], export_dir: str
    ) -> Dict[str, str]:
        ExportFormat.validate(export_formats)
        exported = {}
        if ExportFormat.CHECKPOINT in export_formats:
            path = os.path.join(export_dir, ExportFormat.CHECKPOINT)
            self.export_policy_checkpoint(path)
            exported[ExportFormat.CHECKPOINT] = path
        if ExportFormat.MODEL in export_formats:
            path = os.path.join(export_dir, ExportFormat.MODEL)
            self.export_policy_model(path)
            exported[ExportFormat.MODEL] = path
        if ExportFormat.ONNX in export_formats:
            path = os.path.join(export_dir, ExportFormat.ONNX)
            self.export_policy_model(path, onnx=int(os.getenv("ONNX_OPSET", "11")))
            exported[ExportFormat.ONNX] = path
        return exported

    def import_model(self, import_file: str):
        """Imports a model from import_file.

        Note: Currently, only h5 files are supported.

        Args:
            import_file: The file to import the model from.

        Returns:
            A dict that maps ExportFormats to successfully exported models.
        """
        # Check for existence.
        if not os.path.exists(import_file):
            raise FileNotFoundError(
                "`import_file` '{}' does not exist! Can't import Model.".format(
                    import_file
                )
            )
        # Get the format of the given file.
        import_format = "h5"  # TODO(sven): Support checkpoint loading.

        ExportFormat.validate([import_format])
        if import_format != ExportFormat.H5:
            raise NotImplementedError
        else:
            return self.import_policy_model_from_h5(import_file)

    def __getstate__(self) -> dict:
        state = {}
        if hasattr(self, "workers"):
            state["worker"] = self.workers.local_worker().save()
        # TODO: Experimental functionality: Store contents of replay buffer
        #  to checkpoint, only if user has configured this.
        if self.local_replay_buffer is not None and self.config.get(
            "store_buffer_in_checkpoints"
        ):
            state["local_replay_buffer"] = self.local_replay_buffer.get_state()

        if self.train_exec_impl is not None:
            state["train_exec_impl"] = self.train_exec_impl.shared_metrics.get().save()

        return state

    def __setstate__(self, state: dict):
        if hasattr(self, "workers") and "worker" in state:
            self.workers.local_worker().restore(state["worker"])
            remote_state = ray.put(state["worker"])
            for r in self.workers.remote_workers():
                r.restore.remote(remote_state)
            if self.evaluation_workers:
                # If evaluation workers are used, also restore the policies
                # there in case they are used for evaluation purpose.
                for r in self.evaluation_workers.remote_workers():
                    r.restore.remote(remote_state)
        # If necessary, restore replay data as well.
        if self.local_replay_buffer is not None:
            # TODO: Experimental functionality: Restore contents of replay
            #  buffer from checkpoint, only if user has configured this.
            if self.config.get("store_buffer_in_checkpoints"):
                if "local_replay_buffer" in state:
                    self.local_replay_buffer.set_state(state["local_replay_buffer"])
                else:
                    logger.warning(
                        "`store_buffer_in_checkpoints` is True, but no replay "
                        "data found in state!"
                    )
            elif "local_replay_buffer" in state and log_once(
                "no_store_buffer_in_checkpoints_but_data_found"
            ):
                logger.warning(
                    "`store_buffer_in_checkpoints` is False, but some replay "
                    "data found in state!"
                )

        if self.train_exec_impl is not None:
            self.train_exec_impl.shared_metrics.get().restore(state["train_exec_impl"])

    @DeveloperAPI
    def _create_local_replay_buffer_if_necessary(
        self, config: PartialAlgorithmConfigDict
    ) -> Optional[MultiAgentReplayBuffer]:
        """Create a MultiAgentReplayBuffer instance if necessary.

        Args:
            config: Algorithm-specific configuration data.

        Returns:
            MultiAgentReplayBuffer instance based on trainer config.
            None, if local replay buffer is not needed.
        """
        if not config.get("replay_buffer_config") or config["replay_buffer_config"].get(
            "no_local_replay_buffer" or config.get("no_local_replay_buffer")
        ):
            return

        buffer_type = config["replay_buffer_config"]["type"]
        return from_config(buffer_type, config["replay_buffer_config"])

    @DeveloperAPI
    def _kwargs_for_execution_plan(self):
        kwargs = {}
        if self.local_replay_buffer is not None:
            kwargs["local_replay_buffer"] = self.local_replay_buffer
        return kwargs

    def _run_one_training_iteration(self) -> Tuple[ResultDict, "TrainIterCtx"]:
        """Runs one training iteration (self.iteration will be +1 after this).

        Calls `self.training_step()` repeatedly until the minimum time (sec),
        sample- or training steps have been reached.

        Returns:
            The results dict from the training iteration.
        """
        # In case we are training (in a thread) parallel to evaluation,
        # we may have to re-enable eager mode here (gets disabled in the
        # thread).
        if (
            self.config.get("framework") in ["tf2", "tfe"]
            and not tf.executing_eagerly()
        ):
            tf1.enable_eager_execution()

        results = None
        # Create a step context ...
        with TrainIterCtx(algo=self) as train_iter_ctx:
            # .. so we can query it whether we should stop the iteration loop (e.g.
            # when we have reached `min_time_s_per_iteration`).
            num_recreated = 0
            while not train_iter_ctx.should_stop(results):
                # Try to train one step.
                try:
                    with self._timers[TRAINING_ITERATION_TIMER]:
                        if self.config["_disable_execution_plan_api"]:
                            results = self.training_step()
                        else:
                            results = next(self.train_exec_impl)
                # In case of any failures, try to ignore/recover the failed workers.
                except Exception as e:
                    num_recreated += self.try_recover_from_step_attempt(
                        error=e,
                        worker_set=self.workers,
                        ignore=self.config["ignore_worker_failures"],
                        recreate=self.config["recreate_failed_workers"],
                    )
            results["num_recreated_workers"] = num_recreated

        return results, train_iter_ctx

    def _run_one_evaluation(
        self,
        train_future: Optional[concurrent.futures.ThreadPoolExecutor] = None,
    ) -> ResultDict:
        """Runs evaluation step via `self.evaluate()` and handling worker failures.

        Args:
            train_future: In case, we are training and avaluating in parallel,
                this arg carries the currently running ThreadPoolExecutor
                object that runs the training iteration

        Returns:
            The results dict from the evaluation call.
        """
        eval_results = {
            "evaluation": {
                "episode_reward_max": np.nan,
                "episode_reward_min": np.nan,
                "episode_reward_mean": np.nan,
            }
        }
        eval_results["evaluation"]["num_recreated_workers"] = 0

        eval_func_to_use = (
            self._evaluate_async
            if self.config["enable_async_evaluation"]
            else self.evaluate
        )

        num_recreated = 0

        try:
            if self.config["evaluation_duration"] == "auto":
                assert (
                    train_future is not None
                    and self.config["evaluation_parallel_to_training"]
                )
                unit = self.config["evaluation_duration_unit"]
                eval_results = eval_func_to_use(
                    duration_fn=functools.partial(
                        self._automatic_evaluation_duration_fn,
                        unit,
                        self.config["evaluation_num_workers"],
                        self.config["evaluation_config"],
                        train_future,
                    )
                )
            # Run `self.evaluate()` only once per training iteration.
            else:
                eval_results = eval_func_to_use()

        # In case of any failures, try to ignore/recover the failed evaluation workers.
        except Exception as e:
            num_recreated = self.try_recover_from_step_attempt(
                error=e,
                worker_set=self.evaluation_workers,
                ignore=self.config["evaluation_config"].get("ignore_worker_failures"),
                recreate=self.config["evaluation_config"].get(
                    "recreate_failed_workers"
                ),
            )

        # Add number of healthy evaluation workers after this iteration.
        eval_results["evaluation"]["num_healthy_workers"] = (
            len(self.evaluation_workers.remote_workers())
            if self.evaluation_workers is not None
            else 0
        )
        eval_results["evaluation"]["num_recreated_workers"] = num_recreated

        return eval_results

    def _run_one_training_iteration_and_evaluation_in_parallel(
        self,
    ) -> Tuple[ResultDict, "TrainIterCtx"]:
        """Runs one training iteration and one evaluation step in parallel.

        First starts the training iteration (via `self._run_one_training_iteration()`)
        within a ThreadPoolExecutor, then runs the evaluation step in parallel.
        In auto-duration mode (config.evaluation_duration=auto), makes sure the
        evaluation step takes roughly the same time as the training iteration.

        Returns:
            The accumulated training and evaluation results.
        """
        with concurrent.futures.ThreadPoolExecutor() as executor:
            train_future = executor.submit(lambda: self._run_one_training_iteration())
            # Pass the train_future into `self._run_one_evaluation()` to allow it
            # to run exactly as long as the training iteration takes in case
            # evaluation_duration=auto.
            results = self._run_one_evaluation(train_future)
            # Collect the training results from the future.
            train_results, train_iter_ctx = train_future.result()
            results.update(train_results)

        return results, train_iter_ctx

    @staticmethod
    def _automatic_evaluation_duration_fn(
        unit, num_eval_workers, eval_cfg, train_future, num_units_done
    ):
        # Training is done and we already ran at least one
        # evaluation -> Nothing left to run.
        if num_units_done > 0 and train_future.done():
            return 0
        # Count by episodes. -> Run n more
        # (n=num eval workers).
        elif unit == "episodes":
            return num_eval_workers
        # Count by timesteps. -> Run n*m*p more
        # (n=num eval workers; m=rollout fragment length;
        # p=num-envs-per-worker).
        else:
            return (
                num_eval_workers
                * eval_cfg["rollout_fragment_length"]
                * eval_cfg["num_envs_per_worker"]
            )

    def _compile_iteration_results(
        self, *, episodes_this_iter, step_ctx, iteration_results=None
    ):
        # Return dict.
        results: ResultDict = {}
        iteration_results = iteration_results or {}

        # Evaluation results.
        if "evaluation" in iteration_results:
            results["evaluation"] = iteration_results.pop("evaluation")

        # Custom metrics and episode media.
        results["custom_metrics"] = iteration_results.pop("custom_metrics", {})
        results["episode_media"] = iteration_results.pop("episode_media", {})
        results["num_recreated_workers"] = iteration_results.pop(
            "num_recreated_workers", 0
        )

        # Learner info.
        results["info"] = {LEARNER_INFO: iteration_results}

        # Calculate how many (if any) of older, historical episodes we have to add to
        # `episodes_this_iter` in order to reach the required smoothing window.
        episodes_for_metrics = episodes_this_iter[:]
        missing = self.config["metrics_num_episodes_for_smoothing"] - len(
            episodes_this_iter
        )
        # We have to add some older episodes to reach the smoothing window size.
        if missing > 0:
            episodes_for_metrics = self._episode_history[-missing:] + episodes_this_iter
            assert (
                len(episodes_for_metrics)
                <= self.config["metrics_num_episodes_for_smoothing"]
            )
        # Note that when there are more than `metrics_num_episodes_for_smoothing`
        # episodes in `episodes_for_metrics`, leave them as-is. In this case, we'll
        # compute the stats over that larger number.

        # Add new episodes to our history and make sure it doesn't grow larger than
        # needed.
        self._episode_history.extend(episodes_this_iter)
        self._episode_history = self._episode_history[
            -self.config["metrics_num_episodes_for_smoothing"] :
        ]
        results["sampler_results"] = summarize_episodes(
            episodes_for_metrics,
            episodes_this_iter,
            self.config["keep_per_episode_custom_metrics"],
        )
        # TODO: Don't dump sampler results into top-level.
        results.update(results["sampler_results"])

        results["num_healthy_workers"] = len(self.workers.remote_workers())

        # Train-steps- and env/agent-steps this iteration.
        for c in [
            NUM_AGENT_STEPS_SAMPLED,
            NUM_AGENT_STEPS_TRAINED,
            NUM_ENV_STEPS_SAMPLED,
            NUM_ENV_STEPS_TRAINED,
        ]:
            results[c] = self._counters[c]
        if self._by_agent_steps:
            results[NUM_AGENT_STEPS_SAMPLED + "_this_iter"] = step_ctx.sampled
            results[NUM_AGENT_STEPS_TRAINED + "_this_iter"] = step_ctx.trained
            # TODO: For CQL and other algos, count by trained steps.
            results["timesteps_total"] = self._counters[NUM_AGENT_STEPS_SAMPLED]
            # TODO: Backward compatibility.
            results[STEPS_TRAINED_THIS_ITER_COUNTER] = step_ctx.trained
        else:
            results[NUM_ENV_STEPS_SAMPLED + "_this_iter"] = step_ctx.sampled
            results[NUM_ENV_STEPS_TRAINED + "_this_iter"] = step_ctx.trained
            # TODO: For CQL and other algos, count by trained steps.
            results["timesteps_total"] = self._counters[NUM_ENV_STEPS_SAMPLED]
            # TODO: Backward compatibility.
            results[STEPS_TRAINED_THIS_ITER_COUNTER] = step_ctx.trained
        # TODO: Backward compatibility.
        results["agent_timesteps_total"] = self._counters[NUM_AGENT_STEPS_SAMPLED]

        # Process timer results.
        timers = {}
        for k, timer in self._timers.items():
            timers["{}_time_ms".format(k)] = round(timer.mean * 1000, 3)
            if timer.has_units_processed():
                timers["{}_throughput".format(k)] = round(timer.mean_throughput, 3)
        results["timers"] = timers

        # Process counter results.
        counters = {}
        for k, counter in self._counters.items():
            counters[k] = counter
        results["counters"] = counters
        # TODO: Backward compatibility.
        results["info"].update(counters)

        return results

    def __repr__(self):
        return type(self).__name__

    def _record_usage(self, config):
        """Record the framework and algorithm used.

        Args:
            config: Algorithm config dict.
        """
        record_extra_usage_tag(TagKey.RLLIB_FRAMEWORK, config["framework"])
        record_extra_usage_tag(TagKey.RLLIB_NUM_WORKERS, str(config["num_workers"]))
        alg = self.__class__.__name__
        # We do not want to collect user defined algorithm names.
        if alg not in ALL_ALGORITHMS:
            alg = "USER_DEFINED"
        record_extra_usage_tag(TagKey.RLLIB_ALGORITHM, alg)

    @Deprecated(new="Trainer.compute_single_action()", error=False)
    def compute_action(self, *args, **kwargs):
        return self.compute_single_action(*args, **kwargs)

    @Deprecated(new="logic moved into `self.step()`", error=True)
    def step_attempt(self):
        pass

    @Deprecated(new="construct WorkerSet(...) instance directly", error=False)
    def _make_workers(
        self,
        *,
        env_creator: EnvCreator,
        validate_env: Optional[Callable[[EnvType, EnvContext], None]],
        policy_class: Type[Policy],
        config: AlgorithmConfigDict,
        num_workers: int,
        local_worker: bool = True,
    ) -> WorkerSet:
        return WorkerSet(
            env_creator=env_creator,
            validate_env=validate_env,
            policy_class=policy_class,
            trainer_config=config,
            num_workers=num_workers,
            local_worker=local_worker,
            logdir=self.logdir,
        )

    @staticmethod
    @Deprecated(new="Trainer.validate_config()", error=False)
    def _validate_config(config, trainer_or_none):
        assert trainer_or_none is not None
        return trainer_or_none.validate_config(config)


# TODO: Create a dict that throw a deprecation warning once we have fully moved
#  to AlgorithmConfig() objects (some algos still missing).
COMMON_CONFIG: AlgorithmConfigDict = AlgorithmConfig(Algorithm).to_dict()


class TrainIterCtx:
    def __init__(self, algo: Algorithm):
        self.algo = algo

    def __enter__(self):
        # Before first call to `step()`, `results` is expected to be None ->
        # Start with self.failures=-1 -> set to 0 before the very first call
        # to `self.step()`.
        self.failures = -1

        self.time_start = time.time()
        self.sampled = 0
        self.trained = 0
        self.init_env_steps_sampled = self.algo._counters[NUM_ENV_STEPS_SAMPLED]
        self.init_env_steps_trained = self.algo._counters[NUM_ENV_STEPS_TRAINED]
        self.init_agent_steps_sampled = self.algo._counters[NUM_AGENT_STEPS_SAMPLED]
        self.init_agent_steps_trained = self.algo._counters[NUM_AGENT_STEPS_TRAINED]
        self.failure_tolerance = self.algo.config[
            "num_consecutive_worker_failures_tolerance"
        ]
        return self

    def __exit__(self, *args):
        pass

    def should_stop(self, results):

        # Before first call to `step()`.
        if results is None:
            # Fail after n retries.
            self.failures += 1
            if self.failures > self.failure_tolerance:
                raise RuntimeError(
                    "More than `num_consecutive_worker_failures_tolerance="
                    f"{self.failure_tolerance}` consecutive worker failures! "
                    "Exiting."
                )
            # Continue to very first `step()` call or retry `step()` after
            # a (tolerable) failure.
            return False

        # Stopping criteria: Only when using the `training_iteration`
        # API, b/c for the `exec_plan` API, the logic to stop is
        # already built into the execution plans via the
        # `StandardMetricsReporting` op.
        elif self.algo.config["_disable_execution_plan_api"]:
            if self.algo._by_agent_steps:
                self.sampled = (
                    self.algo._counters[NUM_AGENT_STEPS_SAMPLED]
                    - self.init_agent_steps_sampled
                )
                self.trained = (
                    self.algo._counters[NUM_AGENT_STEPS_TRAINED]
                    - self.init_agent_steps_trained
                )
            else:
                self.sampled = (
                    self.algo._counters[NUM_ENV_STEPS_SAMPLED]
                    - self.init_env_steps_sampled
                )
                self.trained = (
                    self.algo._counters[NUM_ENV_STEPS_TRAINED]
                    - self.init_env_steps_trained
                )

            min_t = self.algo.config["min_time_s_per_iteration"]
            min_sample_ts = self.algo.config["min_sample_timesteps_per_iteration"]
            min_train_ts = self.algo.config["min_train_timesteps_per_iteration"]
            # Repeat if not enough time has passed or if not enough
            # env|train timesteps have been processed (or these min
            # values are not provided by the user).
            if (
                (not min_t or time.time() - self.time_start >= min_t)
                and (not min_sample_ts or self.sampled >= min_sample_ts)
                and (not min_train_ts or self.trained >= min_train_ts)
            ):
                return True
            else:
                return False
        # No errors (we got results != None) -> Return True
        # (meaning: yes, should stop -> no further step attempts).
        else:
            return True