ray/rllib/evaluation/sampler.py

from collections import defaultdict, namedtuple
import logging
import numpy as np
import six.moves.queue as queue
import threading
import time

from ray.util.debug import log_once
from ray.rllib.evaluation.episode import MultiAgentEpisode, _flatten_action
from ray.rllib.evaluation.rollout_metrics import RolloutMetrics
from ray.rllib.evaluation.sample_batch_builder import \
    MultiAgentSampleBatchBuilder
from ray.rllib.policy.policy import clip_action
from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.env.base_env import BaseEnv, ASYNC_RESET_RETURN
from ray.rllib.env.atari_wrappers import get_wrapper_by_cls, MonitorEnv
from ray.rllib.offline import InputReader
from ray.rllib.utils.annotations import override
from ray.rllib.utils.debug import summarize
from ray.rllib.utils.tuple_actions import TupleActions
from ray.rllib.utils.tf_run_builder import TFRunBuilder

logger = logging.getLogger(__name__)

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


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

    def __init__(self):
        self.iters = 0
        self.env_wait_time = 0.0
        self.processing_time = 0.0
        self.inference_time = 0.0

    def get(self):
        return {
            "mean_env_wait_ms": self.env_wait_time * 1000 / self.iters,
            "mean_processing_ms": self.processing_time * 1000 / self.iters,
            "mean_inference_ms": self.inference_time * 1000 / self.iters
        }


class SamplerInput(InputReader):
    """Reads input experiences from an existing sampler."""

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


class SyncSampler(SamplerInput):
    def __init__(self,
                 env,
                 policies,
                 policy_mapping_fn,
                 preprocessors,
                 obs_filters,
                 clip_rewards,
                 unroll_length,
                 callbacks,
                 horizon=None,
                 pack=False,
                 tf_sess=None,
                 clip_actions=True,
                 soft_horizon=False,
                 no_done_at_end=False):
        self.base_env = BaseEnv.to_base_env(env)
        self.unroll_length = unroll_length
        self.horizon = horizon
        self.policies = policies
        self.policy_mapping_fn = policy_mapping_fn
        self.preprocessors = preprocessors
        self.obs_filters = obs_filters
        self.extra_batches = queue.Queue()
        self.perf_stats = PerfStats()
        self.rollout_provider = _env_runner(
            self.base_env, self.extra_batches.put, self.policies,
            self.policy_mapping_fn, self.unroll_length, self.horizon,
            self.preprocessors, self.obs_filters, clip_rewards, clip_actions,
            pack, callbacks, tf_sess, self.perf_stats, soft_horizon,
            no_done_at_end)
        self.metrics_queue = queue.Queue()

    def get_data(self):
        while True:
            item = next(self.rollout_provider)
            if isinstance(item, RolloutMetrics):
                self.metrics_queue.put(item)
            else:
                return item

    def get_metrics(self):
        completed = []
        while True:
            try:
                completed.append(self.metrics_queue.get_nowait()._replace(
                    perf_stats=self.perf_stats.get()))
            except queue.Empty:
                break
        return completed

    def get_extra_batches(self):
        extra = []
        while True:
            try:
                extra.append(self.extra_batches.get_nowait())
            except queue.Empty:
                break
        return extra


class AsyncSampler(threading.Thread, SamplerInput):
    def __init__(self,
                 env,
                 policies,
                 policy_mapping_fn,
                 preprocessors,
                 obs_filters,
                 clip_rewards,
                 unroll_length,
                 callbacks,
                 horizon=None,
                 pack=False,
                 tf_sess=None,
                 clip_actions=True,
                 blackhole_outputs=False,
                 soft_horizon=False,
                 no_done_at_end=False):
        for _, f in obs_filters.items():
            assert getattr(f, "is_concurrent", False), \
                "Observation Filter must support concurrent updates."
        self.base_env = BaseEnv.to_base_env(env)
        threading.Thread.__init__(self)
        self.queue = queue.Queue(5)
        self.extra_batches = queue.Queue()
        self.metrics_queue = queue.Queue()
        self.unroll_length = unroll_length
        self.horizon = horizon
        self.policies = policies
        self.policy_mapping_fn = policy_mapping_fn
        self.preprocessors = preprocessors
        self.obs_filters = obs_filters
        self.clip_rewards = clip_rewards
        self.daemon = True
        self.pack = pack
        self.tf_sess = tf_sess
        self.callbacks = callbacks
        self.clip_actions = clip_actions
        self.blackhole_outputs = blackhole_outputs
        self.soft_horizon = soft_horizon
        self.no_done_at_end = no_done_at_end
        self.perf_stats = PerfStats()
        self.shutdown = False

    def run(self):
        try:
            self._run()
        except BaseException as e:
            self.queue.put(e)
            raise e

    def _run(self):
        if self.blackhole_outputs:
            queue_putter = (lambda x: None)
            extra_batches_putter = (lambda x: None)
        else:
            queue_putter = self.queue.put
            extra_batches_putter = (
                lambda x: self.extra_batches.put(x, timeout=600.0))
        rollout_provider = _env_runner(
            self.base_env, extra_batches_putter, self.policies,
            self.policy_mapping_fn, self.unroll_length, self.horizon,
            self.preprocessors, self.obs_filters, self.clip_rewards,
            self.clip_actions, self.pack, self.callbacks, self.tf_sess,
            self.perf_stats, self.soft_horizon, self.no_done_at_end)
        while not self.shutdown:
            # The timeout variable exists because apparently, if one worker
            # dies, the other workers won't die with it, unless the timeout is
            # set to some large number. This is an empirical observation.
            item = next(rollout_provider)
            if isinstance(item, RolloutMetrics):
                self.metrics_queue.put(item)
            else:
                queue_putter(item)

    def get_data(self):
        if not self.is_alive():
            raise RuntimeError("Sampling thread has died")
        rollout = self.queue.get(timeout=600.0)

        # Propagate errors
        if isinstance(rollout, BaseException):
            raise rollout

        return rollout

    def get_metrics(self):
        completed = []
        while True:
            try:
                completed.append(self.metrics_queue.get_nowait()._replace(
                    perf_stats=self.perf_stats.get()))
            except queue.Empty:
                break
        return completed

    def get_extra_batches(self):
        extra = []
        while True:
            try:
                extra.append(self.extra_batches.get_nowait())
            except queue.Empty:
                break
        return extra


def _env_runner(base_env, extra_batch_callback, policies, policy_mapping_fn,
                unroll_length, horizon, preprocessors, obs_filters,
                clip_rewards, clip_actions, pack, callbacks, tf_sess,
                perf_stats, soft_horizon, no_done_at_end):
    """This implements the common experience collection logic.

    Args:
        base_env (BaseEnv): env implementing BaseEnv.
        extra_batch_callback (fn): function to send extra batch data to.
        policies (dict): Map of policy ids to Policy instances.
        policy_mapping_fn (func): Function that maps agent ids to policy ids.
            This is called when an agent first enters the environment. The
            agent is then "bound" to the returned policy for the episode.
        unroll_length (int): Number of episode steps before `SampleBatch` is
            yielded. Set to infinity to yield complete episodes.
        horizon (int): Horizon of the episode.
        preprocessors (dict): Map of policy id to preprocessor for the
            observations prior to filtering.
        obs_filters (dict): Map of policy id to filter used to process
            observations for the policy.
        clip_rewards (bool): Whether to clip rewards before postprocessing.
        pack (bool): Whether to pack multiple episodes into each batch. This
            guarantees batches will be exactly `unroll_length` in size.
        clip_actions (bool): Whether to clip actions to the space range.
        callbacks (dict): User callbacks to run on episode events.
        tf_sess (Session|None): Optional tensorflow session to use for batching
            TF policy evaluations.
        perf_stats (PerfStats): Record perf stats into this object.
        soft_horizon (bool): Calculate rewards but don't reset the
            environment when the horizon is hit.
        no_done_at_end (bool): Ignore the done=True at the end of the episode
            and instead record done=False.

    Yields:
        rollout (SampleBatch): Object containing state, action, reward,
            terminal condition, and other fields as dictated by `policy`.
    """

    # Try to get Env's max_episode_steps prop. If it doesn't exist, catch
    # error and continue.
    max_episode_steps = None
    try:
        max_episode_steps = base_env.get_unwrapped()[0].spec.max_episode_steps
    except Exception:
        pass

    # Trainer has a given `horizon` setting.
    if horizon:
        # `horizon` is larger than env's limit -> Error and explain how
        # to increase Env's own episode limit.
        if max_episode_steps and horizon > max_episode_steps:
            raise ValueError(
                "Your `horizon` setting ({}) is larger than the Env's own "
                "timestep limit ({})! Try to increase the Env's limit via "
                "setting its `spec.max_episode_steps` property.".format(
                    horizon, max_episode_steps))
    # Otherwise, set Trainer's horizon to env's max-steps.
    elif max_episode_steps:
        horizon = max_episode_steps
        logger.debug(
            "No episode horizon specified, setting it to Env's limit ({}).".
            format(max_episode_steps))
    else:
        horizon = float("inf")
        logger.debug("No episode horizon specified, assuming inf.")

    # Pool of batch builders, which can be shared across episodes to pack
    # trajectory data.
    batch_builder_pool = []

    def get_batch_builder():
        if batch_builder_pool:
            return batch_builder_pool.pop()
        else:
            return MultiAgentSampleBatchBuilder(
                policies, clip_rewards, callbacks.get("on_postprocess_traj"))

    def new_episode():
        episode = MultiAgentEpisode(policies, policy_mapping_fn,
                                    get_batch_builder, extra_batch_callback)
        if callbacks.get("on_episode_start"):
            callbacks["on_episode_start"]({
                "env": base_env,
                "policy": policies,
                "episode": episode,
            })
        return episode

    active_episodes = defaultdict(new_episode)

    while True:
        perf_stats.iters += 1
        t0 = time.time()
        # Get observations from all ready agents
        unfiltered_obs, rewards, dones, infos, off_policy_actions = \
            base_env.poll()
        perf_stats.env_wait_time += time.time() - t0

        if log_once("env_returns"):
            logger.info("Raw obs from env: {}".format(
                summarize(unfiltered_obs)))
            logger.info("Info return from env: {}".format(summarize(infos)))

        # Process observations and prepare for policy evaluation
        t1 = time.time()
        active_envs, to_eval, outputs = _process_observations(
            base_env, policies, batch_builder_pool, active_episodes,
            unfiltered_obs, rewards, dones, infos, off_policy_actions, horizon,
            preprocessors, obs_filters, unroll_length, pack, callbacks,
            soft_horizon, no_done_at_end)
        perf_stats.processing_time += time.time() - t1
        for o in outputs:
            yield o

        # Do batched policy eval
        t2 = time.time()
        eval_results = _do_policy_eval(tf_sess, to_eval, policies,
                                       active_episodes)
        perf_stats.inference_time += time.time() - t2

        # Process results and update episode state
        t3 = time.time()
        actions_to_send = _process_policy_eval_results(
            to_eval, eval_results, active_episodes, active_envs,
            off_policy_actions, policies, clip_actions)
        perf_stats.processing_time += time.time() - t3

        # Return computed actions to ready envs. We also send to envs that have
        # taken off-policy actions; those envs are free to ignore the action.
        t4 = time.time()
        base_env.send_actions(actions_to_send)
        perf_stats.env_wait_time += time.time() - t4


def _process_observations(base_env, policies, batch_builder_pool,
                          active_episodes, unfiltered_obs, rewards, dones,
                          infos, off_policy_actions, horizon, preprocessors,
                          obs_filters, unroll_length, pack, callbacks,
                          soft_horizon, no_done_at_end):
    """Record new data from the environment and prepare for policy evaluation.

    Returns:
        active_envs: set of non-terminated env ids
        to_eval: map of policy_id to list of agent PolicyEvalData
        outputs: list of metrics and samples to return from the sampler
    """

    active_envs = set()
    to_eval = defaultdict(list)
    outputs = []
    large_batch_threshold = max(1000, unroll_length * 10) if \
        unroll_length != float("inf") else 5000

    # For each environment
    for env_id, agent_obs in unfiltered_obs.items():
        new_episode = env_id not in active_episodes
        episode = active_episodes[env_id]
        if not new_episode:
            episode.length += 1
            episode.batch_builder.count += 1
            episode._add_agent_rewards(rewards[env_id])

        if (episode.batch_builder.total() > large_batch_threshold
                and log_once("large_batch_warning")):
            logger.warning(
                "More than {} observations for {} env steps ".format(
                    episode.batch_builder.total(),
                    episode.batch_builder.count) + "are buffered in "
                "the sampler. If this is more than you expected, check that "
                "that you set a horizon on your environment correctly and that"
                " it terminates at some point. "
                "Note: In multi-agent environments, `sample_batch_size` sets "
                "the batch size based on environment steps, not the steps of "
                "individual agents, which can result in unexpectedly large "
                "batches. Also, you may be in evaluation waiting for your Env "
                "to terminate (batch_mode=`complete_episodes`). Make sure it "
                "does at some point.")

        # Check episode termination conditions
        if dones[env_id]["__all__"] or episode.length >= horizon:
            hit_horizon = (episode.length >= horizon
                           and not dones[env_id]["__all__"])
            all_done = True
            atari_metrics = _fetch_atari_metrics(base_env)
            if atari_metrics is not None:
                for m in atari_metrics:
                    outputs.append(
                        m._replace(custom_metrics=episode.custom_metrics))
            else:
                outputs.append(
                    RolloutMetrics(episode.length, episode.total_reward,
                                   dict(episode.agent_rewards),
                                   episode.custom_metrics, {},
                                   episode.hist_data))
        else:
            hit_horizon = False
            all_done = False
            active_envs.add(env_id)

        # For each agent in the environment.
        for agent_id, raw_obs in agent_obs.items():
            policy_id = episode.policy_for(agent_id)
            prep_obs = _get_or_raise(preprocessors,
                                     policy_id).transform(raw_obs)
            if log_once("prep_obs"):
                logger.info("Preprocessed obs: {}".format(summarize(prep_obs)))

            filtered_obs = _get_or_raise(obs_filters, policy_id)(prep_obs)
            if log_once("filtered_obs"):
                logger.info("Filtered obs: {}".format(summarize(filtered_obs)))

            agent_done = bool(all_done or dones[env_id].get(agent_id))
            if not agent_done:
                to_eval[policy_id].append(
                    PolicyEvalData(env_id, agent_id, filtered_obs,
                                   infos[env_id].get(agent_id, {}),
                                   episode.rnn_state_for(agent_id),
                                   episode.last_action_for(agent_id),
                                   rewards[env_id][agent_id] or 0.0))

            last_observation = episode.last_observation_for(agent_id)
            episode._set_last_observation(agent_id, filtered_obs)
            episode._set_last_raw_obs(agent_id, raw_obs)
            episode._set_last_info(agent_id, infos[env_id].get(agent_id, {}))

            # Record transition info if applicable
            if (last_observation is not None and infos[env_id].get(
                    agent_id, {}).get("training_enabled", True)):
                episode.batch_builder.add_values(
                    agent_id,
                    policy_id,
                    t=episode.length - 1,
                    eps_id=episode.episode_id,
                    agent_index=episode._agent_index(agent_id),
                    obs=last_observation,
                    actions=episode.last_action_for(agent_id),
                    rewards=rewards[env_id][agent_id],
                    prev_actions=episode.prev_action_for(agent_id),
                    prev_rewards=episode.prev_reward_for(agent_id),
                    dones=(False if (no_done_at_end
                                     or (hit_horizon and soft_horizon)) else
                           agent_done),
                    infos=infos[env_id].get(agent_id, {}),
                    new_obs=filtered_obs,
                    **episode.last_pi_info_for(agent_id))

        # Invoke the step callback after the step is logged to the episode
        if callbacks.get("on_episode_step"):
            callbacks["on_episode_step"]({"env": base_env, "episode": episode})

        # Cut the batch if we're not packing multiple episodes into one,
        # or if we've exceeded the requested batch size.
        if episode.batch_builder.has_pending_agent_data():
            if dones[env_id]["__all__"] and not no_done_at_end:
                episode.batch_builder.check_missing_dones()
            if (all_done and not pack) or \
                    episode.batch_builder.count >= unroll_length:
                outputs.append(episode.batch_builder.build_and_reset(episode))
            elif all_done:
                # Make sure postprocessor stays within one episode
                episode.batch_builder.postprocess_batch_so_far(episode)

        if all_done:
            # Handle episode termination
            batch_builder_pool.append(episode.batch_builder)
            if callbacks.get("on_episode_end"):
                callbacks["on_episode_end"]({
                    "env": base_env,
                    "policy": policies,
                    "episode": episode
                })
            if hit_horizon and soft_horizon:
                episode.soft_reset()
                resetted_obs = agent_obs
            else:
                del active_episodes[env_id]
                resetted_obs = base_env.try_reset(env_id)
            if resetted_obs is None:
                # Reset not supported, drop this env from the ready list
                if horizon != float("inf"):
                    raise ValueError(
                        "Setting episode horizon requires reset() support "
                        "from the environment.")
            elif resetted_obs != ASYNC_RESET_RETURN:
                # Creates a new episode if this is not async return
                # If reset is async, we will get its result in some future poll
                episode = active_episodes[env_id]
                for agent_id, raw_obs in resetted_obs.items():
                    policy_id = episode.policy_for(agent_id)
                    policy = _get_or_raise(policies, policy_id)
                    prep_obs = _get_or_raise(preprocessors,
                                             policy_id).transform(raw_obs)
                    filtered_obs = _get_or_raise(obs_filters,
                                                 policy_id)(prep_obs)
                    episode._set_last_observation(agent_id, filtered_obs)
                    to_eval[policy_id].append(
                        PolicyEvalData(
                            env_id, agent_id, filtered_obs,
                            episode.last_info_for(agent_id) or {},
                            episode.rnn_state_for(agent_id),
                            np.zeros_like(
                                _flatten_action(policy.action_space.sample())),
                            0.0))

    return active_envs, to_eval, outputs


def _do_policy_eval(tf_sess, to_eval, policies, active_episodes):
    """Call compute actions on observation batches to get next actions.

    Returns:
        eval_results: dict of policy to compute_action() outputs.
    """

    eval_results = {}

    if tf_sess:
        builder = TFRunBuilder(tf_sess, "policy_eval")
        pending_fetches = {}
    else:
        builder = None

    if log_once("compute_actions_input"):
        logger.info("Inputs to compute_actions():\n\n{}\n".format(
            summarize(to_eval)))

    for policy_id, eval_data in to_eval.items():
        rnn_in = [t.rnn_state for t in eval_data]
        policy = _get_or_raise(policies, policy_id)
        if builder and (policy.compute_actions.__code__ is
                        TFPolicy.compute_actions.__code__):
            rnn_in_cols = _to_column_format(rnn_in)
            # TODO(ekl): how can we make info batch available to TF code?
            # TODO(sven): Return dict from _build_compute_actions.
            # it's becoming more and more unclear otherwise, what's where in
            # the return tuple.
            pending_fetches[policy_id] = policy._build_compute_actions(
                builder,
                obs_batch=[t.obs for t in eval_data],
                state_batches=rnn_in_cols,
                prev_action_batch=[t.prev_action for t in eval_data],
                prev_reward_batch=[t.prev_reward for t in eval_data],
                timestep=policy.global_timestep)
        else:
            # TODO(sven): Does this work for LSTM torch?
            rnn_in_cols = [
                np.stack([row[i] for row in rnn_in])
                for i in range(len(rnn_in[0]))
            ]
            eval_results[policy_id] = policy.compute_actions(
                [t.obs for t in eval_data],
                state_batches=rnn_in_cols,
                prev_action_batch=[t.prev_action for t in eval_data],
                prev_reward_batch=[t.prev_reward for t in eval_data],
                info_batch=[t.info for t in eval_data],
                episodes=[active_episodes[t.env_id] for t in eval_data],
                timestep=policy.global_timestep)
    if builder:
        for k, v in pending_fetches.items():
            eval_results[k] = builder.get(v)

    if log_once("compute_actions_result"):
        logger.info("Outputs of compute_actions():\n\n{}\n".format(
            summarize(eval_results)))

    return eval_results


def _process_policy_eval_results(to_eval, eval_results, active_episodes,
                                 active_envs, off_policy_actions, policies,
                                 clip_actions):
    """Process the output of policy neural network evaluation.

    Records policy evaluation results into the given episode objects and
    returns replies to send back to agents in the env.

    Returns:
        actions_to_send: nested dict of env id -> agent id -> agent replies.
    """

    actions_to_send = defaultdict(dict)
    for env_id in active_envs:
        actions_to_send[env_id] = {}  # at minimum send empty dict

    for policy_id, eval_data in to_eval.items():
        rnn_in_cols = _to_column_format([t.rnn_state for t in eval_data])
        actions, rnn_out_cols, pi_info_cols = eval_results[policy_id][:3]
        if len(rnn_in_cols) != len(rnn_out_cols):
            raise ValueError("Length of RNN in did not match RNN out, got: "
                             "{} vs {}".format(rnn_in_cols, rnn_out_cols))
        # Add RNN state info
        for f_i, column in enumerate(rnn_in_cols):
            pi_info_cols["state_in_{}".format(f_i)] = column
        for f_i, column in enumerate(rnn_out_cols):
            pi_info_cols["state_out_{}".format(f_i)] = column
        # Save output rows
        actions = _unbatch_tuple_actions(actions)
        policy = _get_or_raise(policies, policy_id)
        for i, action in enumerate(actions):
            env_id = eval_data[i].env_id
            agent_id = eval_data[i].agent_id
            if clip_actions:
                actions_to_send[env_id][agent_id] = clip_action(
                    action, policy.action_space)
            else:
                actions_to_send[env_id][agent_id] = action
            episode = active_episodes[env_id]
            episode._set_rnn_state(agent_id, [c[i] for c in rnn_out_cols])
            episode._set_last_pi_info(
                agent_id, {k: v[i]
                           for k, v in pi_info_cols.items()})
            if env_id in off_policy_actions and \
                    agent_id in off_policy_actions[env_id]:
                episode._set_last_action(agent_id,
                                         off_policy_actions[env_id][agent_id])
            else:
                episode._set_last_action(agent_id, action)

    return actions_to_send


def _fetch_atari_metrics(base_env):
    """Atari games have multiple logical episodes, one per life.

    However for metrics reporting we count full episodes all lives included.
    """
    unwrapped = base_env.get_unwrapped()
    if not unwrapped:
        return None
    atari_out = []
    for u in unwrapped:
        monitor = get_wrapper_by_cls(u, MonitorEnv)
        if not monitor:
            return None
        for eps_rew, eps_len in monitor.next_episode_results():
            atari_out.append(RolloutMetrics(eps_len, eps_rew))
    return atari_out


def _unbatch_tuple_actions(action_batch):
    # convert list of batches -> batch of lists
    if isinstance(action_batch, TupleActions):
        out = []
        for j in range(len(action_batch.batches[0])):
            out.append([
                action_batch.batches[i][j]
                for i in range(len(action_batch.batches))
            ])
        return out
    return action_batch


def _to_column_format(rnn_state_rows):
    num_cols = len(rnn_state_rows[0])
    return [[row[i] for row in rnn_state_rows] for i in range(num_cols)]


def _get_or_raise(mapping, policy_id):
    """Returns a Policy object under key `policy_id` in `mapping`.

    Throws an error if `policy_id` cannot be found.

    Returns:
        Policy: The found Policy object.
    """
    if policy_id not in mapping:
        raise ValueError(
            "Could not find policy for agent: agent policy id `{}` not "
            "in policy map keys {}.".format(policy_id, mapping.keys()))
    return mapping[policy_id]