mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
1190 lines
50 KiB
Python
1190 lines
50 KiB
Python
from abc import abstractmethod, ABCMeta
|
|
from collections import defaultdict, namedtuple
|
|
import logging
|
|
import numpy as np
|
|
import queue
|
|
import threading
|
|
import time
|
|
import tree # pip install dm_tree
|
|
from typing import Any, Callable, Dict, List, Iterator, Optional, Set, Tuple,\
|
|
Type, TYPE_CHECKING, Union
|
|
|
|
from ray.util.debug import log_once
|
|
from ray.rllib.evaluation.collectors.sample_collector import \
|
|
SampleCollector
|
|
from ray.rllib.evaluation.collectors.simple_list_collector import \
|
|
SimpleListCollector
|
|
from ray.rllib.evaluation.episode import MultiAgentEpisode
|
|
from ray.rllib.evaluation.rollout_metrics import RolloutMetrics
|
|
from ray.rllib.evaluation.sample_batch_builder import \
|
|
MultiAgentSampleBatchBuilder
|
|
from ray.rllib.env.base_env import BaseEnv, ASYNC_RESET_RETURN
|
|
from ray.rllib.env.wrappers.atari_wrappers import get_wrapper_by_cls, \
|
|
MonitorEnv
|
|
from ray.rllib.models.preprocessors import Preprocessor
|
|
from ray.rllib.offline import InputReader
|
|
from ray.rllib.policy.policy import Policy
|
|
from ray.rllib.policy.policy_map import PolicyMap
|
|
from ray.rllib.policy.sample_batch import SampleBatch
|
|
from ray.rllib.utils.annotations import override, DeveloperAPI
|
|
from ray.rllib.utils.debug import summarize
|
|
from ray.rllib.utils.deprecation import deprecation_warning
|
|
from ray.rllib.utils.filter import Filter
|
|
from ray.rllib.utils.numpy import convert_to_numpy
|
|
from ray.rllib.utils.spaces.space_utils import clip_action, \
|
|
unsquash_action, unbatch
|
|
from ray.rllib.utils.typing import SampleBatchType, AgentID, PolicyID, \
|
|
EnvObsType, EnvInfoDict, EnvID, MultiEnvDict, EnvActionType, \
|
|
TensorStructType
|
|
|
|
if TYPE_CHECKING:
|
|
from ray.rllib.agents.callbacks import DefaultCallbacks
|
|
from ray.rllib.evaluation.observation_function import ObservationFunction
|
|
from ray.rllib.evaluation.rollout_worker import RolloutWorker
|
|
from ray.rllib.utils import try_import_tf
|
|
_, tf, _ = try_import_tf()
|
|
from gym.envs.classic_control.rendering import SimpleImageViewer
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
PolicyEvalData = namedtuple("PolicyEvalData", [
|
|
"env_id", "agent_id", "obs", "info", "rnn_state", "prev_action",
|
|
"prev_reward"
|
|
])
|
|
|
|
# A batch of RNN states with dimensions [state_index, batch, state_object].
|
|
StateBatch = List[List[Any]]
|
|
|
|
|
|
class NewEpisodeDefaultDict(defaultdict):
|
|
def __missing__(self, env_id):
|
|
if self.default_factory is None:
|
|
raise KeyError(env_id)
|
|
else:
|
|
ret = self[env_id] = self.default_factory(env_id)
|
|
return ret
|
|
|
|
|
|
class _PerfStats:
|
|
"""Sampler perf stats that will be included in rollout metrics."""
|
|
|
|
def __init__(self):
|
|
self.iters = 0
|
|
self.raw_obs_processing_time = 0.0
|
|
self.inference_time = 0.0
|
|
self.action_processing_time = 0.0
|
|
self.env_wait_time = 0.0
|
|
self.env_render_time = 0.0
|
|
|
|
def get(self):
|
|
# Mean multiplicator (1000 = ms -> sec).
|
|
factor = 1000 / self.iters
|
|
return {
|
|
# Raw observation preprocessing.
|
|
"mean_raw_obs_processing_ms": self.raw_obs_processing_time *
|
|
factor,
|
|
# Computing actions through policy.
|
|
"mean_inference_ms": self.inference_time * factor,
|
|
# Processing actions (to be sent to env, e.g. clipping).
|
|
"mean_action_processing_ms": self.action_processing_time * factor,
|
|
# Waiting for environment (during poll).
|
|
"mean_env_wait_ms": self.env_wait_time * factor,
|
|
# Environment rendering (False by default).
|
|
"mean_env_render_ms": self.env_render_time * factor,
|
|
}
|
|
|
|
|
|
@DeveloperAPI
|
|
class SamplerInput(InputReader, metaclass=ABCMeta):
|
|
"""Reads input experiences from an existing sampler."""
|
|
|
|
@override(InputReader)
|
|
def next(self) -> SampleBatchType:
|
|
batches = [self.get_data()]
|
|
batches.extend(self.get_extra_batches())
|
|
if len(batches) > 1:
|
|
return batches[0].concat_samples(batches)
|
|
else:
|
|
return batches[0]
|
|
|
|
@abstractmethod
|
|
@DeveloperAPI
|
|
def get_data(self) -> SampleBatchType:
|
|
raise NotImplementedError
|
|
|
|
@abstractmethod
|
|
@DeveloperAPI
|
|
def get_metrics(self) -> List[RolloutMetrics]:
|
|
raise NotImplementedError
|
|
|
|
@abstractmethod
|
|
@DeveloperAPI
|
|
def get_extra_batches(self) -> List[SampleBatchType]:
|
|
raise NotImplementedError
|
|
|
|
|
|
@DeveloperAPI
|
|
class SyncSampler(SamplerInput):
|
|
"""Sync SamplerInput that collects experiences when `get_data()` is called.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
worker: "RolloutWorker",
|
|
env: BaseEnv,
|
|
clip_rewards: Union[bool, float],
|
|
rollout_fragment_length: int,
|
|
count_steps_by: str = "env_steps",
|
|
callbacks: "DefaultCallbacks",
|
|
horizon: int = None,
|
|
multiple_episodes_in_batch: bool = False,
|
|
normalize_actions: bool = True,
|
|
clip_actions: bool = False,
|
|
soft_horizon: bool = False,
|
|
no_done_at_end: bool = False,
|
|
observation_fn: "ObservationFunction" = None,
|
|
sample_collector_class: Optional[Type[SampleCollector]] = None,
|
|
render: bool = False,
|
|
# Obsolete.
|
|
policies=None,
|
|
policy_mapping_fn=None,
|
|
preprocessors=None,
|
|
obs_filters=None,
|
|
tf_sess=None,
|
|
):
|
|
"""Initializes a SyncSampler object.
|
|
|
|
Args:
|
|
worker (RolloutWorker): The RolloutWorker that will use this
|
|
Sampler for sampling.
|
|
env (Env): Any Env object. Will be converted into an RLlib BaseEnv.
|
|
clip_rewards (Union[bool, float]): True for +/-1.0 clipping,
|
|
actual float value for +/- value clipping. False for no
|
|
clipping.
|
|
rollout_fragment_length (int): The length of a fragment to collect
|
|
before building a SampleBatch from the data and resetting
|
|
the SampleBatchBuilder object.
|
|
callbacks (Callbacks): The Callbacks object to use when episode
|
|
events happen during rollout.
|
|
horizon (Optional[int]): Hard-reset the Env
|
|
multiple_episodes_in_batch (bool): Whether to pack multiple
|
|
episodes into each batch. This guarantees batches will be
|
|
exactly `rollout_fragment_length` in size.
|
|
normalize_actions (bool): Whether to normalize actions to the
|
|
action space's bounds.
|
|
clip_actions (bool): Whether to clip actions according to the
|
|
given action_space's bounds.
|
|
soft_horizon (bool): If True, calculate bootstrapped values as if
|
|
episode had ended, but don't physically reset the environment
|
|
when the horizon is hit.
|
|
no_done_at_end (bool): Ignore the done=True at the end of the
|
|
episode and instead record done=False.
|
|
observation_fn (Optional[ObservationFunction]): Optional
|
|
multi-agent observation func to use for preprocessing
|
|
observations.
|
|
sample_collector_class (Optional[Type[SampleCollector]]): An
|
|
optional Samplecollector sub-class to use to collect, store,
|
|
and retrieve environment-, model-, and sampler data.
|
|
render (bool): Whether to try to render the environment after each
|
|
step.
|
|
"""
|
|
# All of the following arguments are deprecated. They will instead be
|
|
# provided via the passed in `worker` arg, e.g. `worker.policy_map`.
|
|
if log_once("deprecated_sync_sampler_args"):
|
|
if policies is not None:
|
|
deprecation_warning(old="policies")
|
|
if policy_mapping_fn is not None:
|
|
deprecation_warning(old="policy_mapping_fn")
|
|
if preprocessors is not None:
|
|
deprecation_warning(old="preprocessors")
|
|
if obs_filters is not None:
|
|
deprecation_warning(old="obs_filters")
|
|
if tf_sess is not None:
|
|
deprecation_warning(old="tf_sess")
|
|
|
|
self.base_env = BaseEnv.to_base_env(env)
|
|
self.rollout_fragment_length = rollout_fragment_length
|
|
self.horizon = horizon
|
|
self.extra_batches = queue.Queue()
|
|
self.perf_stats = _PerfStats()
|
|
if not sample_collector_class:
|
|
sample_collector_class = SimpleListCollector
|
|
self.sample_collector = sample_collector_class(
|
|
worker.policy_map,
|
|
clip_rewards,
|
|
callbacks,
|
|
multiple_episodes_in_batch,
|
|
rollout_fragment_length,
|
|
count_steps_by=count_steps_by)
|
|
self.render = render
|
|
|
|
# Create the rollout generator to use for calls to `get_data()`.
|
|
self._env_runner = _env_runner(
|
|
worker, self.base_env, self.extra_batches.put, self.horizon,
|
|
normalize_actions, clip_actions, multiple_episodes_in_batch,
|
|
callbacks, self.perf_stats, soft_horizon, no_done_at_end,
|
|
observation_fn, self.sample_collector, self.render)
|
|
self.metrics_queue = queue.Queue()
|
|
|
|
@override(SamplerInput)
|
|
def get_data(self) -> SampleBatchType:
|
|
while True:
|
|
item = next(self._env_runner)
|
|
if isinstance(item, RolloutMetrics):
|
|
self.metrics_queue.put(item)
|
|
else:
|
|
return item
|
|
|
|
@override(SamplerInput)
|
|
def get_metrics(self) -> List[RolloutMetrics]:
|
|
completed = []
|
|
while True:
|
|
try:
|
|
completed.append(self.metrics_queue.get_nowait()._replace(
|
|
perf_stats=self.perf_stats.get()))
|
|
except queue.Empty:
|
|
break
|
|
return completed
|
|
|
|
@override(SamplerInput)
|
|
def get_extra_batches(self) -> List[SampleBatchType]:
|
|
extra = []
|
|
while True:
|
|
try:
|
|
extra.append(self.extra_batches.get_nowait())
|
|
except queue.Empty:
|
|
break
|
|
return extra
|
|
|
|
|
|
@DeveloperAPI
|
|
class AsyncSampler(threading.Thread, SamplerInput):
|
|
"""Async SamplerInput that collects experiences in thread and queues them.
|
|
|
|
Once started, experiences are continuously collected and put into a Queue,
|
|
from where they can be unqueued by the caller of `get_data()`.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
worker: "RolloutWorker",
|
|
env: BaseEnv,
|
|
clip_rewards: Union[bool, float],
|
|
rollout_fragment_length: int,
|
|
count_steps_by: str = "env_steps",
|
|
callbacks: "DefaultCallbacks",
|
|
horizon: Optional[int] = None,
|
|
multiple_episodes_in_batch: bool = False,
|
|
normalize_actions: bool = True,
|
|
clip_actions: bool = False,
|
|
blackhole_outputs: bool = False,
|
|
soft_horizon: bool = False,
|
|
no_done_at_end: bool = False,
|
|
observation_fn: Optional["ObservationFunction"] = None,
|
|
sample_collector_class: Optional[Type[SampleCollector]] = None,
|
|
render: bool = False,
|
|
# Obsolete.
|
|
policies=None,
|
|
policy_mapping_fn=None,
|
|
preprocessors=None,
|
|
obs_filters=None,
|
|
tf_sess=None,
|
|
):
|
|
"""Initializes a AsyncSampler object.
|
|
|
|
Args:
|
|
worker (RolloutWorker): The RolloutWorker that will use this
|
|
Sampler for sampling.
|
|
env (Env): Any Env object. Will be converted into an RLlib BaseEnv.
|
|
clip_rewards (Union[bool, float]): True for +/-1.0 clipping,
|
|
actual float value for +/- value clipping. False for no
|
|
clipping.
|
|
rollout_fragment_length (int): The length of a fragment to collect
|
|
before building a SampleBatch from the data and resetting
|
|
the SampleBatchBuilder object.
|
|
count_steps_by (str): Either "env_steps" or "agent_steps".
|
|
Refers to the unit of `rollout_fragment_length`.
|
|
callbacks (Callbacks): The Callbacks object to use when episode
|
|
events happen during rollout.
|
|
horizon: Hard-reset the Env after this many timesteps.
|
|
multiple_episodes_in_batch (bool): Whether to pack multiple
|
|
episodes into each batch. This guarantees batches will be
|
|
exactly `rollout_fragment_length` in size.
|
|
normalize_actions (bool): Whether to normalize actions to the
|
|
action space's bounds.
|
|
clip_actions (bool): Whether to clip actions according to the
|
|
given action_space's bounds.
|
|
blackhole_outputs (bool): Whether to collect samples, but then
|
|
not further process or store them (throw away all samples).
|
|
soft_horizon (bool): If True, calculate bootstrapped values as if
|
|
episode had ended, but don't physically reset the environment
|
|
when the horizon is hit.
|
|
no_done_at_end (bool): Ignore the done=True at the end of the
|
|
episode and instead record done=False.
|
|
observation_fn (Optional[ObservationFunction]): Optional
|
|
multi-agent observation func to use for preprocessing
|
|
observations.
|
|
sample_collector_class (Optional[Type[SampleCollector]]): An
|
|
optional Samplecollector sub-class to use to collect, store,
|
|
and retrieve environment-, model-, and sampler data.
|
|
render (bool): Whether to try to render the environment after each
|
|
step.
|
|
"""
|
|
# All of the following arguments are deprecated. They will instead be
|
|
# provided via the passed in `worker` arg, e.g. `worker.policy_map`.
|
|
if log_once("deprecated_async_sampler_args"):
|
|
if policies is not None:
|
|
deprecation_warning(old="policies")
|
|
if policy_mapping_fn is not None:
|
|
deprecation_warning(old="policy_mapping_fn")
|
|
if preprocessors is not None:
|
|
deprecation_warning(old="preprocessors")
|
|
if obs_filters is not None:
|
|
deprecation_warning(old="obs_filters")
|
|
if tf_sess is not None:
|
|
deprecation_warning(old="tf_sess")
|
|
|
|
self.worker = worker
|
|
|
|
for _, f in worker.filters.items():
|
|
assert getattr(f, "is_concurrent", False), \
|
|
"Observation Filter must support concurrent updates."
|
|
|
|
self.base_env = BaseEnv.to_base_env(env)
|
|
threading.Thread.__init__(self)
|
|
self.queue = queue.Queue(5)
|
|
self.extra_batches = queue.Queue()
|
|
self.metrics_queue = queue.Queue()
|
|
self.rollout_fragment_length = rollout_fragment_length
|
|
self.horizon = horizon
|
|
self.clip_rewards = clip_rewards
|
|
self.daemon = True
|
|
self.multiple_episodes_in_batch = multiple_episodes_in_batch
|
|
self.callbacks = callbacks
|
|
self.normalize_actions = normalize_actions
|
|
self.clip_actions = clip_actions
|
|
self.blackhole_outputs = blackhole_outputs
|
|
self.soft_horizon = soft_horizon
|
|
self.no_done_at_end = no_done_at_end
|
|
self.perf_stats = _PerfStats()
|
|
self.shutdown = False
|
|
self.observation_fn = observation_fn
|
|
self.render = render
|
|
if not sample_collector_class:
|
|
sample_collector_class = SimpleListCollector
|
|
self.sample_collector = sample_collector_class(
|
|
self.worker.policy_map,
|
|
self.clip_rewards,
|
|
self.callbacks,
|
|
self.multiple_episodes_in_batch,
|
|
self.rollout_fragment_length,
|
|
count_steps_by=count_steps_by)
|
|
|
|
@override(threading.Thread)
|
|
def run(self):
|
|
try:
|
|
self._run()
|
|
except BaseException as e:
|
|
self.queue.put(e)
|
|
raise e
|
|
|
|
def _run(self):
|
|
if self.blackhole_outputs:
|
|
queue_putter = (lambda x: None)
|
|
extra_batches_putter = (lambda x: None)
|
|
else:
|
|
queue_putter = self.queue.put
|
|
extra_batches_putter = (
|
|
lambda x: self.extra_batches.put(x, timeout=600.0))
|
|
env_runner = _env_runner(
|
|
self.worker, self.base_env, extra_batches_putter, self.horizon,
|
|
self.normalize_actions, self.clip_actions,
|
|
self.multiple_episodes_in_batch, self.callbacks, self.perf_stats,
|
|
self.soft_horizon, self.no_done_at_end, self.observation_fn,
|
|
self.sample_collector, self.render)
|
|
while not self.shutdown:
|
|
# The timeout variable exists because apparently, if one worker
|
|
# dies, the other workers won't die with it, unless the timeout is
|
|
# set to some large number. This is an empirical observation.
|
|
item = next(env_runner)
|
|
if isinstance(item, RolloutMetrics):
|
|
self.metrics_queue.put(item)
|
|
else:
|
|
queue_putter(item)
|
|
|
|
@override(SamplerInput)
|
|
def get_data(self) -> SampleBatchType:
|
|
if not self.is_alive():
|
|
raise RuntimeError("Sampling thread has died")
|
|
rollout = self.queue.get(timeout=600.0)
|
|
|
|
# Propagate errors.
|
|
if isinstance(rollout, BaseException):
|
|
raise rollout
|
|
|
|
return rollout
|
|
|
|
@override(SamplerInput)
|
|
def get_metrics(self) -> List[RolloutMetrics]:
|
|
completed = []
|
|
while True:
|
|
try:
|
|
completed.append(self.metrics_queue.get_nowait()._replace(
|
|
perf_stats=self.perf_stats.get()))
|
|
except queue.Empty:
|
|
break
|
|
return completed
|
|
|
|
@override(SamplerInput)
|
|
def get_extra_batches(self) -> List[SampleBatchType]:
|
|
extra = []
|
|
while True:
|
|
try:
|
|
extra.append(self.extra_batches.get_nowait())
|
|
except queue.Empty:
|
|
break
|
|
return extra
|
|
|
|
|
|
def _env_runner(
|
|
worker: "RolloutWorker",
|
|
base_env: BaseEnv,
|
|
extra_batch_callback: Callable[[SampleBatchType], None],
|
|
horizon: Optional[int],
|
|
normalize_actions: bool,
|
|
clip_actions: bool,
|
|
multiple_episodes_in_batch: bool,
|
|
callbacks: "DefaultCallbacks",
|
|
perf_stats: _PerfStats,
|
|
soft_horizon: bool,
|
|
no_done_at_end: bool,
|
|
observation_fn: "ObservationFunction",
|
|
sample_collector: Optional[SampleCollector] = None,
|
|
render: bool = None,
|
|
) -> Iterator[SampleBatchType]:
|
|
"""This implements the common experience collection logic.
|
|
|
|
Args:
|
|
worker (RolloutWorker): Reference to the current rollout worker.
|
|
base_env (BaseEnv): Env implementing BaseEnv.
|
|
extra_batch_callback (fn): function to send extra batch data to.
|
|
horizon: Horizon of the episode.
|
|
multiple_episodes_in_batch (bool): Whether to pack multiple
|
|
episodes into each batch. This guarantees batches will be exactly
|
|
`rollout_fragment_length` in size.
|
|
normalize_actions (bool): Whether to normalize actions to the action
|
|
space's bounds.
|
|
clip_actions (bool): Whether to clip actions to the space range.
|
|
callbacks (DefaultCallbacks): User callbacks to run on episode events.
|
|
perf_stats (_PerfStats): Record perf stats into this object.
|
|
soft_horizon (bool): Calculate rewards but don't reset the
|
|
environment when the horizon is hit.
|
|
no_done_at_end (bool): Ignore the done=True at the end of the episode
|
|
and instead record done=False.
|
|
observation_fn (ObservationFunction): Optional multi-agent
|
|
observation func to use for preprocessing observations.
|
|
sample_collector (Optional[SampleCollector]): An optional
|
|
SampleCollector object to use.
|
|
render (bool): Whether to try to render the environment after each
|
|
step.
|
|
|
|
Yields:
|
|
rollout (SampleBatch): Object containing state, action, reward,
|
|
terminal condition, and other fields as dictated by `policy`.
|
|
"""
|
|
|
|
# May be populated with used for image rendering
|
|
simple_image_viewer: Optional["SimpleImageViewer"] = None
|
|
|
|
# Try to get Env's `max_episode_steps` prop. If it doesn't exist, ignore
|
|
# error and continue with max_episode_steps=None.
|
|
max_episode_steps = None
|
|
try:
|
|
max_episode_steps = base_env.get_unwrapped()[0].spec.max_episode_steps
|
|
except Exception:
|
|
pass
|
|
|
|
# Trainer has a given `horizon` setting.
|
|
if horizon:
|
|
# `horizon` is larger than env's limit.
|
|
if max_episode_steps and horizon > max_episode_steps:
|
|
# Try to override the env's own max-step setting with our horizon.
|
|
# If this won't work, throw an error.
|
|
try:
|
|
base_env.get_unwrapped()[0].spec.max_episode_steps = horizon
|
|
base_env.get_unwrapped()[0]._max_episode_steps = horizon
|
|
except Exception:
|
|
raise ValueError(
|
|
"Your `horizon` setting ({}) is larger than the Env's own "
|
|
"timestep limit ({}), which seems to be unsettable! Try "
|
|
"to increase the Env's built-in limit to be at least as "
|
|
"large as your wanted `horizon`.".format(
|
|
horizon, max_episode_steps))
|
|
# Otherwise, set Trainer's horizon to env's max-steps.
|
|
elif max_episode_steps:
|
|
horizon = max_episode_steps
|
|
logger.debug(
|
|
"No episode horizon specified, setting it to Env's limit ({}).".
|
|
format(max_episode_steps))
|
|
# No horizon/max_episode_steps -> Episodes may be infinitely long.
|
|
else:
|
|
horizon = float("inf")
|
|
logger.debug("No episode horizon specified, assuming inf.")
|
|
|
|
# Pool of batch builders, which can be shared across episodes to pack
|
|
# trajectory data.
|
|
batch_builder_pool: List[MultiAgentSampleBatchBuilder] = []
|
|
|
|
def get_batch_builder():
|
|
if batch_builder_pool:
|
|
return batch_builder_pool.pop()
|
|
else:
|
|
return None
|
|
|
|
def new_episode(env_id):
|
|
episode = MultiAgentEpisode(
|
|
worker.policy_map,
|
|
worker.policy_mapping_fn,
|
|
get_batch_builder,
|
|
extra_batch_callback,
|
|
env_id=env_id,
|
|
worker=worker,
|
|
)
|
|
# Call each policy's Exploration.on_episode_start method.
|
|
# Note: This may break the exploration (e.g. ParameterNoise) of
|
|
# policies in the `policy_map` that have not been recently used
|
|
# (and are therefore stashed to disk). However, we certainly do not
|
|
# want to loop through all (even stashed) policies here as that
|
|
# would counter the purpose of the LRU policy caching.
|
|
for p in worker.policy_map.cache.values():
|
|
if getattr(p, "exploration", None) is not None:
|
|
p.exploration.on_episode_start(
|
|
policy=p,
|
|
environment=base_env,
|
|
episode=episode,
|
|
tf_sess=p.get_session())
|
|
callbacks.on_episode_start(
|
|
worker=worker,
|
|
base_env=base_env,
|
|
policies=worker.policy_map,
|
|
episode=episode,
|
|
env_index=env_id,
|
|
)
|
|
return episode
|
|
|
|
active_episodes: Dict[EnvID, MultiAgentEpisode] = \
|
|
NewEpisodeDefaultDict(new_episode)
|
|
|
|
while True:
|
|
perf_stats.iters += 1
|
|
t0 = time.time()
|
|
# Get observations from all ready agents.
|
|
# types: MultiEnvDict, MultiEnvDict, MultiEnvDict, MultiEnvDict, ...
|
|
unfiltered_obs, rewards, dones, infos, off_policy_actions = \
|
|
base_env.poll()
|
|
perf_stats.env_wait_time += time.time() - t0
|
|
|
|
if log_once("env_returns"):
|
|
logger.info("Raw obs from env: {}".format(
|
|
summarize(unfiltered_obs)))
|
|
logger.info("Info return from env: {}".format(summarize(infos)))
|
|
|
|
# Process observations and prepare for policy evaluation.
|
|
t1 = time.time()
|
|
# types: Set[EnvID], Dict[PolicyID, List[PolicyEvalData]],
|
|
# List[Union[RolloutMetrics, SampleBatchType]]
|
|
active_envs, to_eval, outputs = \
|
|
_process_observations(
|
|
worker=worker,
|
|
base_env=base_env,
|
|
active_episodes=active_episodes,
|
|
unfiltered_obs=unfiltered_obs,
|
|
rewards=rewards,
|
|
dones=dones,
|
|
infos=infos,
|
|
horizon=horizon,
|
|
multiple_episodes_in_batch=multiple_episodes_in_batch,
|
|
callbacks=callbacks,
|
|
soft_horizon=soft_horizon,
|
|
no_done_at_end=no_done_at_end,
|
|
observation_fn=observation_fn,
|
|
sample_collector=sample_collector,
|
|
)
|
|
perf_stats.raw_obs_processing_time += time.time() - t1
|
|
for o in outputs:
|
|
yield o
|
|
|
|
# Do batched policy eval (accross vectorized envs).
|
|
t2 = time.time()
|
|
# types: Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]]
|
|
eval_results = _do_policy_eval(
|
|
to_eval=to_eval,
|
|
policies=worker.policy_map,
|
|
sample_collector=sample_collector,
|
|
active_episodes=active_episodes,
|
|
)
|
|
perf_stats.inference_time += time.time() - t2
|
|
|
|
# Process results and update episode state.
|
|
t3 = time.time()
|
|
actions_to_send: Dict[EnvID, Dict[AgentID, EnvActionType]] = \
|
|
_process_policy_eval_results(
|
|
to_eval=to_eval,
|
|
eval_results=eval_results,
|
|
active_episodes=active_episodes,
|
|
active_envs=active_envs,
|
|
off_policy_actions=off_policy_actions,
|
|
policies=worker.policy_map,
|
|
normalize_actions=normalize_actions,
|
|
clip_actions=clip_actions,
|
|
)
|
|
perf_stats.action_processing_time += time.time() - t3
|
|
|
|
# Return computed actions to ready envs. We also send to envs that have
|
|
# taken off-policy actions; those envs are free to ignore the action.
|
|
t4 = time.time()
|
|
base_env.send_actions(actions_to_send)
|
|
perf_stats.env_wait_time += time.time() - t4
|
|
|
|
# Try to render the env, if required.
|
|
if render:
|
|
t5 = time.time()
|
|
# Render can either return an RGB image (uint8 [w x h x 3] numpy
|
|
# array) or take care of rendering itself (returning True).
|
|
rendered = base_env.try_render()
|
|
# Rendering returned an image -> Display it in a SimpleImageViewer.
|
|
if isinstance(rendered, np.ndarray) and len(rendered.shape) == 3:
|
|
# ImageViewer not defined yet, try to create one.
|
|
if simple_image_viewer is None:
|
|
try:
|
|
from gym.envs.classic_control.rendering import \
|
|
SimpleImageViewer
|
|
simple_image_viewer = SimpleImageViewer()
|
|
except (ImportError, ModuleNotFoundError):
|
|
render = False # disable rendering
|
|
logger.warning(
|
|
"Could not import gym.envs.classic_control."
|
|
"rendering! Try `pip install gym[all]`.")
|
|
if simple_image_viewer:
|
|
simple_image_viewer.imshow(rendered)
|
|
elif rendered not in [True, False, None]:
|
|
raise ValueError(
|
|
"The env's ({base_env}) `try_render()` method returned an"
|
|
" unsupported value! Make sure you either return a "
|
|
"uint8/w x h x 3 (RGB) image or handle rendering in a "
|
|
"window and then return `True`.")
|
|
perf_stats.env_render_time += time.time() - t5
|
|
|
|
|
|
def _process_observations(
|
|
*,
|
|
worker: "RolloutWorker",
|
|
base_env: BaseEnv,
|
|
active_episodes: Dict[EnvID, MultiAgentEpisode],
|
|
unfiltered_obs: Dict[EnvID, Dict[AgentID, EnvObsType]],
|
|
rewards: Dict[EnvID, Dict[AgentID, float]],
|
|
dones: Dict[EnvID, Dict[AgentID, bool]],
|
|
infos: Dict[EnvID, Dict[AgentID, EnvInfoDict]],
|
|
horizon: int,
|
|
multiple_episodes_in_batch: bool,
|
|
callbacks: "DefaultCallbacks",
|
|
soft_horizon: bool,
|
|
no_done_at_end: bool,
|
|
observation_fn: "ObservationFunction",
|
|
sample_collector: SampleCollector,
|
|
) -> Tuple[Set[EnvID], Dict[PolicyID, List[PolicyEvalData]], List[Union[
|
|
RolloutMetrics, SampleBatchType]]]:
|
|
"""Record new data from the environment and prepare for policy evaluation.
|
|
|
|
Args:
|
|
worker (RolloutWorker): Reference to the current rollout worker.
|
|
base_env (BaseEnv): Env implementing BaseEnv.
|
|
active_episodes (Dict[EnvID, MultiAgentEpisode]): Mapping from
|
|
episode ID to currently ongoing MultiAgentEpisode object.
|
|
unfiltered_obs (dict): Doubly keyed dict of env-ids -> agent ids
|
|
-> unfiltered observation tensor, returned by a `BaseEnv.poll()`
|
|
call.
|
|
rewards (dict): Doubly keyed dict of env-ids -> agent ids ->
|
|
rewards tensor, returned by a `BaseEnv.poll()` call.
|
|
dones (dict): Doubly keyed dict of env-ids -> agent ids ->
|
|
boolean done flags, returned by a `BaseEnv.poll()` call.
|
|
infos (dict): Doubly keyed dict of env-ids -> agent ids ->
|
|
info dicts, returned by a `BaseEnv.poll()` call.
|
|
horizon (int): Horizon of the episode.
|
|
multiple_episodes_in_batch (bool): Whether to pack multiple
|
|
episodes into each batch. This guarantees batches will be exactly
|
|
`rollout_fragment_length` in size.
|
|
callbacks (DefaultCallbacks): User callbacks to run on episode events.
|
|
soft_horizon (bool): Calculate rewards but don't reset the
|
|
environment when the horizon is hit.
|
|
no_done_at_end (bool): Ignore the done=True at the end of the episode
|
|
and instead record done=False.
|
|
observation_fn (ObservationFunction): Optional multi-agent
|
|
observation func to use for preprocessing observations.
|
|
sample_collector (SampleCollector): The SampleCollector object
|
|
used to store and retrieve environment samples.
|
|
|
|
Returns:
|
|
Tuple:
|
|
- active_envs: Set of non-terminated env ids.
|
|
- to_eval: Map of policy_id to list of agent PolicyEvalData.
|
|
- outputs: List of metrics and samples to return from the sampler.
|
|
"""
|
|
|
|
# Output objects.
|
|
active_envs: Set[EnvID] = set()
|
|
to_eval: Dict[PolicyID, List[PolicyEvalData]] = defaultdict(list)
|
|
outputs: List[Union[RolloutMetrics, SampleBatchType]] = []
|
|
|
|
# For each (vectorized) sub-environment.
|
|
# types: EnvID, Dict[AgentID, EnvObsType]
|
|
for env_id, all_agents_obs in unfiltered_obs.items():
|
|
is_new_episode: bool = env_id not in active_episodes
|
|
episode: MultiAgentEpisode = active_episodes[env_id]
|
|
|
|
if not is_new_episode:
|
|
sample_collector.episode_step(episode)
|
|
episode._add_agent_rewards(rewards[env_id])
|
|
|
|
# Check episode termination conditions.
|
|
if dones[env_id]["__all__"] or episode.length >= horizon:
|
|
hit_horizon = (episode.length >= horizon
|
|
and not dones[env_id]["__all__"])
|
|
all_agents_done = True
|
|
atari_metrics: List[RolloutMetrics] = _fetch_atari_metrics(
|
|
base_env)
|
|
if atari_metrics is not None:
|
|
for m in atari_metrics:
|
|
outputs.append(
|
|
m._replace(custom_metrics=episode.custom_metrics))
|
|
else:
|
|
outputs.append(
|
|
RolloutMetrics(episode.length, episode.total_reward,
|
|
dict(episode.agent_rewards),
|
|
episode.custom_metrics, {},
|
|
episode.hist_data, episode.media))
|
|
# Check whether we have to create a fake-last observation
|
|
# for some agents (the environment is not required to do so if
|
|
# dones[__all__]=True).
|
|
for ag_id in episode.get_agents():
|
|
if not episode.last_done_for(
|
|
ag_id) and ag_id not in all_agents_obs:
|
|
# Create a fake (all-0s) observation.
|
|
obs_sp = worker.policy_map[episode.policy_for(
|
|
ag_id)].observation_space
|
|
obs_sp = getattr(obs_sp, "original_space", obs_sp)
|
|
all_agents_obs[ag_id] = tree.map_structure(
|
|
np.zeros_like, obs_sp.sample())
|
|
else:
|
|
hit_horizon = False
|
|
all_agents_done = False
|
|
active_envs.add(env_id)
|
|
|
|
# Custom observation function is applied before preprocessing.
|
|
if observation_fn:
|
|
all_agents_obs: Dict[AgentID, EnvObsType] = observation_fn(
|
|
agent_obs=all_agents_obs,
|
|
worker=worker,
|
|
base_env=base_env,
|
|
policies=worker.policy_map,
|
|
episode=episode)
|
|
if not isinstance(all_agents_obs, dict):
|
|
raise ValueError(
|
|
"observe() must return a dict of agent observations")
|
|
|
|
# For each agent in the environment.
|
|
# types: AgentID, EnvObsType
|
|
for agent_id, raw_obs in all_agents_obs.items():
|
|
assert agent_id != "__all__"
|
|
|
|
last_observation: EnvObsType = episode.last_observation_for(
|
|
agent_id)
|
|
agent_done = bool(all_agents_done or dones[env_id].get(agent_id))
|
|
|
|
# A new agent (initial obs) is already done -> Skip entirely.
|
|
if last_observation is None and agent_done:
|
|
continue
|
|
|
|
policy_id: PolicyID = episode.policy_for(agent_id)
|
|
|
|
preprocessor = _get_or_raise(worker.preprocessors, policy_id)
|
|
prep_obs: EnvObsType = raw_obs
|
|
if preprocessor is not None:
|
|
prep_obs = preprocessor.transform(raw_obs)
|
|
if log_once("prep_obs"):
|
|
logger.info("Preprocessed obs: {}".format(
|
|
summarize(prep_obs)))
|
|
filtered_obs: EnvObsType = _get_or_raise(worker.filters,
|
|
policy_id)(prep_obs)
|
|
if log_once("filtered_obs"):
|
|
logger.info("Filtered obs: {}".format(summarize(filtered_obs)))
|
|
|
|
episode._set_last_observation(agent_id, filtered_obs)
|
|
episode._set_last_raw_obs(agent_id, raw_obs)
|
|
episode._set_last_done(agent_id, agent_done)
|
|
# Infos from the environment.
|
|
agent_infos = infos[env_id].get(agent_id, {})
|
|
episode._set_last_info(agent_id, agent_infos)
|
|
|
|
# Record transition info if applicable.
|
|
if last_observation is None:
|
|
sample_collector.add_init_obs(episode, agent_id, env_id,
|
|
policy_id, episode.length - 1,
|
|
filtered_obs)
|
|
elif agent_infos is None or agent_infos.get(
|
|
"training_enabled", True):
|
|
# Add actions, rewards, next-obs to collectors.
|
|
values_dict = {
|
|
SampleBatch.T: episode.length - 1,
|
|
SampleBatch.ENV_ID: env_id,
|
|
SampleBatch.AGENT_INDEX: episode._agent_index(agent_id),
|
|
# Action (slot 0) taken at timestep t.
|
|
SampleBatch.ACTIONS: episode.last_action_for(agent_id),
|
|
# Reward received after taking a at timestep t.
|
|
SampleBatch.REWARDS: rewards[env_id].get(agent_id, 0.0),
|
|
# After taking action=a, did we reach terminal?
|
|
SampleBatch.DONES: (False
|
|
if (no_done_at_end
|
|
or (hit_horizon and soft_horizon))
|
|
else agent_done),
|
|
# Next observation.
|
|
SampleBatch.NEXT_OBS: filtered_obs,
|
|
}
|
|
# Add extra-action-fetches to collectors.
|
|
pol = worker.policy_map[policy_id]
|
|
for key, value in episode.last_pi_info_for(agent_id).items():
|
|
if key in pol.view_requirements:
|
|
values_dict[key] = value
|
|
# Env infos for this agent.
|
|
if "infos" in pol.view_requirements:
|
|
values_dict["infos"] = agent_infos
|
|
sample_collector.add_action_reward_next_obs(
|
|
episode.episode_id, agent_id, env_id, policy_id,
|
|
agent_done, values_dict)
|
|
|
|
if not agent_done:
|
|
item = PolicyEvalData(
|
|
env_id, agent_id, filtered_obs, agent_infos, None
|
|
if last_observation is None else
|
|
episode.rnn_state_for(agent_id), None
|
|
if last_observation is None else
|
|
episode.last_action_for(agent_id), rewards[env_id].get(
|
|
agent_id, 0.0))
|
|
to_eval[policy_id].append(item)
|
|
|
|
# Invoke the `on_episode_step` callback after the step is logged
|
|
# to the episode.
|
|
# Exception: The very first env.poll() call causes the env to get reset
|
|
# (no step taken yet, just a single starting observation logged).
|
|
# We need to skip this callback in this case.
|
|
if episode.length > 0:
|
|
callbacks.on_episode_step(
|
|
worker=worker,
|
|
base_env=base_env,
|
|
policies=worker.policy_map,
|
|
episode=episode,
|
|
env_index=env_id)
|
|
|
|
# Episode is done for all agents (dones[__all__] == True)
|
|
# or we hit the horizon.
|
|
if all_agents_done:
|
|
is_done = dones[env_id]["__all__"]
|
|
check_dones = is_done and not no_done_at_end
|
|
|
|
# If, we are not allowed to pack the next episode into the same
|
|
# SampleBatch (batch_mode=complete_episodes) -> Build the
|
|
# MultiAgentBatch from a single episode and add it to "outputs".
|
|
# Otherwise, just postprocess and continue collecting across
|
|
# episodes.
|
|
ma_sample_batch = sample_collector.postprocess_episode(
|
|
episode,
|
|
is_done=is_done or (hit_horizon and not soft_horizon),
|
|
check_dones=check_dones,
|
|
build=not multiple_episodes_in_batch)
|
|
if ma_sample_batch:
|
|
outputs.append(ma_sample_batch)
|
|
|
|
# Call each (in-memory) policy's Exploration.on_episode_end
|
|
# method.
|
|
# Note: This may break the exploration (e.g. ParameterNoise) of
|
|
# policies in the `policy_map` that have not been recently used
|
|
# (and are therefore stashed to disk). However, we certainly do not
|
|
# want to loop through all (even stashed) policies here as that
|
|
# would counter the purpose of the LRU policy caching.
|
|
for p in worker.policy_map.cache.values():
|
|
if getattr(p, "exploration", None) is not None:
|
|
p.exploration.on_episode_end(
|
|
policy=p,
|
|
environment=base_env,
|
|
episode=episode,
|
|
tf_sess=p.get_session())
|
|
# Call custom on_episode_end callback.
|
|
callbacks.on_episode_end(
|
|
worker=worker,
|
|
base_env=base_env,
|
|
policies=worker.policy_map,
|
|
episode=episode,
|
|
env_index=env_id,
|
|
)
|
|
# Horizon hit and we have a soft horizon (no hard env reset).
|
|
if hit_horizon and soft_horizon:
|
|
episode.soft_reset()
|
|
resetted_obs: Dict[AgentID, EnvObsType] = all_agents_obs
|
|
else:
|
|
del active_episodes[env_id]
|
|
resetted_obs: Dict[AgentID, EnvObsType] = base_env.try_reset(
|
|
env_id)
|
|
# Reset not supported, drop this env from the ready list.
|
|
if resetted_obs is None:
|
|
if horizon != float("inf"):
|
|
raise ValueError(
|
|
"Setting episode horizon requires reset() support "
|
|
"from the environment.")
|
|
# Creates a new episode if this is not async return.
|
|
# If reset is async, we will get its result in some future poll.
|
|
elif resetted_obs != ASYNC_RESET_RETURN:
|
|
new_episode: MultiAgentEpisode = active_episodes[env_id]
|
|
if observation_fn:
|
|
resetted_obs: Dict[AgentID, EnvObsType] = observation_fn(
|
|
agent_obs=resetted_obs,
|
|
worker=worker,
|
|
base_env=base_env,
|
|
policies=worker.policy_map,
|
|
episode=new_episode)
|
|
# types: AgentID, EnvObsType
|
|
for agent_id, raw_obs in resetted_obs.items():
|
|
policy_id: PolicyID = new_episode.policy_for(agent_id)
|
|
preproccessor = _get_or_raise(worker.preprocessors,
|
|
policy_id)
|
|
|
|
prep_obs: EnvObsType = raw_obs
|
|
if preproccessor is not None:
|
|
prep_obs = preproccessor.transform(raw_obs)
|
|
filtered_obs: EnvObsType = _get_or_raise(
|
|
worker.filters, policy_id)(prep_obs)
|
|
new_episode._set_last_raw_obs(agent_id, raw_obs)
|
|
new_episode._set_last_observation(agent_id, filtered_obs)
|
|
|
|
# Add initial obs to buffer.
|
|
sample_collector.add_init_obs(
|
|
new_episode, agent_id, env_id, policy_id,
|
|
new_episode.length - 1, filtered_obs)
|
|
|
|
item = PolicyEvalData(
|
|
env_id, agent_id, filtered_obs,
|
|
episode.last_info_for(agent_id) or {},
|
|
episode.rnn_state_for(agent_id), None, 0.0)
|
|
to_eval[policy_id].append(item)
|
|
|
|
# Try to build something.
|
|
if multiple_episodes_in_batch:
|
|
sample_batches = \
|
|
sample_collector.try_build_truncated_episode_multi_agent_batch()
|
|
if sample_batches:
|
|
outputs.extend(sample_batches)
|
|
|
|
return active_envs, to_eval, outputs
|
|
|
|
|
|
def _do_policy_eval(
|
|
*,
|
|
to_eval: Dict[PolicyID, List[PolicyEvalData]],
|
|
policies: PolicyMap,
|
|
sample_collector,
|
|
active_episodes: Dict[EnvID, MultiAgentEpisode],
|
|
) -> Dict[PolicyID, Tuple[TensorStructType, StateBatch, dict]]:
|
|
"""Call compute_actions on collected episode/model data to get next action.
|
|
|
|
Args:
|
|
to_eval (Dict[PolicyID, List[PolicyEvalData]]): Mapping of policy
|
|
IDs to lists of PolicyEvalData objects (items in these lists will
|
|
be the batch's items for the model forward pass).
|
|
policies (Dict[PolicyID, Policy]): Mapping from policy ID to Policy
|
|
obj.
|
|
sample_collector (SampleCollector): The SampleCollector object to use.
|
|
active_episodes (Dict[EnvID, MultiAgentEpisode]): Mapping of
|
|
|
|
Returns:
|
|
eval_results: dict of policy to compute_action() outputs.
|
|
"""
|
|
|
|
eval_results: Dict[PolicyID, TensorStructType] = {}
|
|
|
|
if log_once("compute_actions_input"):
|
|
logger.info("Inputs to compute_actions():\n\n{}\n".format(
|
|
summarize(to_eval)))
|
|
|
|
for policy_id, eval_data in to_eval.items():
|
|
# In case the policyID has been removed from this worker, we need to
|
|
# re-assign policy_id and re-lookup the Policy object to use.
|
|
try:
|
|
policy: Policy = _get_or_raise(policies, policy_id)
|
|
except ValueError:
|
|
# Important: Get the policy_mapping_fn from the active
|
|
# Episode as the policy_mapping_fn from the worker may
|
|
# have already been changed (mapping fn stay constant
|
|
# within one episode).
|
|
episode = active_episodes[eval_data[0].env_id]
|
|
policy_id = episode.policy_mapping_fn(
|
|
eval_data[0].agent_id, episode, worker=episode.worker)
|
|
policy: Policy = _get_or_raise(policies, policy_id)
|
|
|
|
input_dict = sample_collector.get_inference_input_dict(policy_id)
|
|
eval_results[policy_id] = \
|
|
policy.compute_actions_from_input_dict(
|
|
input_dict,
|
|
timestep=policy.global_timestep,
|
|
episodes=[active_episodes[t.env_id] for t in eval_data])
|
|
|
|
if log_once("compute_actions_result"):
|
|
logger.info("Outputs of compute_actions():\n\n{}\n".format(
|
|
summarize(eval_results)))
|
|
|
|
return eval_results
|
|
|
|
|
|
def _process_policy_eval_results(
|
|
*,
|
|
to_eval: Dict[PolicyID, List[PolicyEvalData]],
|
|
eval_results: Dict[PolicyID, Tuple[TensorStructType, StateBatch,
|
|
dict]],
|
|
active_episodes: Dict[EnvID, MultiAgentEpisode],
|
|
active_envs: Set[int],
|
|
off_policy_actions: MultiEnvDict,
|
|
policies: Dict[PolicyID, Policy],
|
|
normalize_actions: bool,
|
|
clip_actions: bool,
|
|
) -> Dict[EnvID, Dict[AgentID, EnvActionType]]:
|
|
"""Process the output of policy neural network evaluation.
|
|
|
|
Records policy evaluation results into the given episode objects and
|
|
returns replies to send back to agents in the env.
|
|
|
|
Args:
|
|
to_eval (Dict[PolicyID, List[PolicyEvalData]]): Mapping of policy IDs
|
|
to lists of PolicyEvalData objects.
|
|
eval_results (Dict[PolicyID, List]): Mapping of policy IDs to list of
|
|
actions, rnn-out states, extra-action-fetches dicts.
|
|
active_episodes (Dict[EnvID, MultiAgentEpisode]): Mapping from
|
|
episode ID to currently ongoing MultiAgentEpisode object.
|
|
active_envs (Set[int]): Set of non-terminated env ids.
|
|
off_policy_actions (dict): Doubly keyed dict of env-ids -> agent ids ->
|
|
off-policy-action, returned by a `BaseEnv.poll()` call.
|
|
policies (Dict[PolicyID, Policy]): Mapping from policy ID to Policy.
|
|
normalize_actions (bool): Whether to normalize actions to the action
|
|
space's bounds.
|
|
clip_actions (bool): Whether to clip actions to the action space's
|
|
bounds.
|
|
|
|
Returns:
|
|
actions_to_send: Nested dict of env id -> agent id -> actions to be
|
|
sent to Env (np.ndarrays).
|
|
"""
|
|
|
|
actions_to_send: Dict[EnvID, Dict[AgentID, EnvActionType]] = \
|
|
defaultdict(dict)
|
|
|
|
# types: int
|
|
for env_id in active_envs:
|
|
actions_to_send[env_id] = {} # at minimum send empty dict
|
|
|
|
# types: PolicyID, List[PolicyEvalData]
|
|
for policy_id, eval_data in to_eval.items():
|
|
actions: TensorStructType = eval_results[policy_id][0]
|
|
actions = convert_to_numpy(actions)
|
|
|
|
rnn_out_cols: StateBatch = eval_results[policy_id][1]
|
|
pi_info_cols: dict = eval_results[policy_id][2]
|
|
|
|
# In case actions is a list (representing the 0th dim of a batch of
|
|
# primitive actions), try converting it first.
|
|
if isinstance(actions, list):
|
|
actions = np.array(actions)
|
|
|
|
# Store RNN state ins/outs and extra-action fetches to episode.
|
|
for f_i, column in enumerate(rnn_out_cols):
|
|
pi_info_cols["state_out_{}".format(f_i)] = column
|
|
|
|
policy: Policy = _get_or_raise(policies, policy_id)
|
|
# Split action-component batches into single action rows.
|
|
actions: List[EnvActionType] = unbatch(actions)
|
|
# types: int, EnvActionType
|
|
for i, action in enumerate(actions):
|
|
# Normalize, if necessary.
|
|
if normalize_actions:
|
|
action_to_send = unsquash_action(action,
|
|
policy.action_space_struct)
|
|
# Clip, if necessary.
|
|
elif clip_actions:
|
|
action_to_send = clip_action(action,
|
|
policy.action_space_struct)
|
|
else:
|
|
action_to_send = action
|
|
|
|
env_id: int = eval_data[i].env_id
|
|
agent_id: AgentID = eval_data[i].agent_id
|
|
episode: MultiAgentEpisode = active_episodes[env_id]
|
|
episode._set_rnn_state(agent_id, [c[i] for c in rnn_out_cols])
|
|
episode._set_last_pi_info(
|
|
agent_id, {k: v[i]
|
|
for k, v in pi_info_cols.items()})
|
|
if env_id in off_policy_actions and \
|
|
agent_id in off_policy_actions[env_id]:
|
|
episode._set_last_action(agent_id,
|
|
off_policy_actions[env_id][agent_id])
|
|
else:
|
|
episode._set_last_action(agent_id, action)
|
|
|
|
assert agent_id not in actions_to_send[env_id]
|
|
actions_to_send[env_id][agent_id] = action_to_send
|
|
|
|
return actions_to_send
|
|
|
|
|
|
def _fetch_atari_metrics(base_env: BaseEnv) -> List[RolloutMetrics]:
|
|
"""Atari games have multiple logical episodes, one per life.
|
|
|
|
However, for metrics reporting we count full episodes, all lives included.
|
|
"""
|
|
unwrapped = base_env.get_unwrapped()
|
|
if not unwrapped:
|
|
return None
|
|
atari_out = []
|
|
for u in unwrapped:
|
|
monitor = get_wrapper_by_cls(u, MonitorEnv)
|
|
if not monitor:
|
|
return None
|
|
for eps_rew, eps_len in monitor.next_episode_results():
|
|
atari_out.append(RolloutMetrics(eps_len, eps_rew))
|
|
return atari_out
|
|
|
|
|
|
def _to_column_format(rnn_state_rows: List[List[Any]]) -> StateBatch:
|
|
num_cols = len(rnn_state_rows[0])
|
|
return [[row[i] for row in rnn_state_rows] for i in range(num_cols)]
|
|
|
|
|
|
def _get_or_raise(mapping: Dict[PolicyID, Union[Policy, Preprocessor, Filter]],
|
|
policy_id: PolicyID) -> Union[Policy, Preprocessor, Filter]:
|
|
"""Returns an object under key `policy_id` in `mapping`.
|
|
|
|
Args:
|
|
mapping (Dict[PolicyID, Union[Policy, Preprocessor, Filter]]): The
|
|
mapping dict from policy id (str) to actual object (Policy,
|
|
Preprocessor, etc.).
|
|
policy_id (str): The policy ID to lookup.
|
|
|
|
Returns:
|
|
Union[Policy, Preprocessor, Filter]: The found object.
|
|
|
|
Raises:
|
|
ValueError: If `policy_id` cannot be found in `mapping`.
|
|
"""
|
|
if policy_id not in mapping:
|
|
raise ValueError(
|
|
"Could not find policy for agent: PolicyID `{}` not found "
|
|
"in policy map, whose keys are `{}`.".format(
|
|
policy_id, mapping.keys()))
|
|
return mapping[policy_id]
|