Source code for ray.rllib.evaluation.rollout_worker

import random
import numpy as np
import gym
import logging
import pickle
import os

import ray
from ray.util.debug import log_once, disable_log_once_globally, \
    enable_periodic_logging
from ray.util.iter import ParallelIteratorWorker
from ray.rllib.env.atari_wrappers import wrap_deepmind, is_atari
from ray.rllib.env.base_env import BaseEnv
from ray.rllib.env.env_context import EnvContext
from ray.rllib.env.external_env import ExternalEnv
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.env.external_multi_agent_env import ExternalMultiAgentEnv
from ray.rllib.env.vector_env import VectorEnv
from ray.rllib.evaluation.sampler import AsyncSampler, SyncSampler
from ray.rllib.policy.sample_batch import MultiAgentBatch, DEFAULT_POLICY_ID
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.policy.torch_policy import TorchPolicy
from ray.rllib.offline import NoopOutput, IOContext, OutputWriter, InputReader
from ray.rllib.offline.is_estimator import ImportanceSamplingEstimator
from ray.rllib.offline.wis_estimator import WeightedImportanceSamplingEstimator
from ray.rllib.models import ModelCatalog
from ray.rllib.models.preprocessors import NoPreprocessor
from ray.rllib.utils import merge_dicts
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils.debug import summarize
from ray.rllib.utils.filter import get_filter
from ray.rllib.utils.sgd import do_minibatch_sgd
from ray.rllib.utils.tf_run_builder import TFRunBuilder
from ray.rllib.utils import try_import_tf, try_import_torch

tf = try_import_tf()
torch, _ = try_import_torch()

logger = logging.getLogger(__name__)

# Handle to the current rollout worker, which will be set to the most recently
# created RolloutWorker in this process. This can be helpful to access in
# custom env or policy classes for debugging or advanced use cases.
_global_worker = None


@DeveloperAPI
def get_global_worker():
    """Returns a handle to the active rollout worker in this process."""

    global _global_worker
    return _global_worker


[docs]@DeveloperAPI class RolloutWorker(ParallelIteratorWorker): """Common experience collection class. This class wraps a policy instance and an environment class to collect experiences from the environment. You can create many replicas of this class as Ray actors to scale RL training. This class supports vectorized and multi-agent policy evaluation (e.g., VectorEnv, MultiAgentEnv, etc.) Examples: >>> # Create a rollout worker and using it to collect experiences. >>> worker = RolloutWorker( ... env_creator=lambda _: gym.make("CartPole-v0"), ... policy=PGTFPolicy) >>> print(worker.sample()) SampleBatch({ "obs": [[...]], "actions": [[...]], "rewards": [[...]], "dones": [[...]], "new_obs": [[...]]}) >>> # Creating a multi-agent rollout worker >>> worker = RolloutWorker( ... env_creator=lambda _: MultiAgentTrafficGrid(num_cars=25), ... policies={ ... # Use an ensemble of two policies for car agents ... "car_policy1": ... (PGTFPolicy, Box(...), Discrete(...), {"gamma": 0.99}), ... "car_policy2": ... (PGTFPolicy, Box(...), Discrete(...), {"gamma": 0.95}), ... # Use a single shared policy for all traffic lights ... "traffic_light_policy": ... (PGTFPolicy, Box(...), Discrete(...), {}), ... }, ... policy_mapping_fn=lambda agent_id: ... random.choice(["car_policy1", "car_policy2"]) ... if agent_id.startswith("car_") else "traffic_light_policy") >>> print(worker.sample()) MultiAgentBatch({ "car_policy1": SampleBatch(...), "car_policy2": SampleBatch(...), "traffic_light_policy": SampleBatch(...)}) """ @DeveloperAPI @classmethod def as_remote(cls, num_cpus=None, num_gpus=None, memory=None, object_store_memory=None, resources=None): return ray.remote( num_cpus=num_cpus, num_gpus=num_gpus, memory=memory, object_store_memory=object_store_memory, resources=resources)(cls) @DeveloperAPI def __init__(self, env_creator, policy, policy_mapping_fn=None, policies_to_train=None, tf_session_creator=None, rollout_fragment_length=100, batch_mode="truncate_episodes", episode_horizon=None, preprocessor_pref="deepmind", sample_async=False, compress_observations=False, num_envs=1, observation_fn=None, observation_filter="NoFilter", clip_rewards=None, clip_actions=True, env_config=None, model_config=None, policy_config=None, worker_index=0, num_workers=0, monitor_path=None, log_dir=None, log_level=None, callbacks=None, input_creator=lambda ioctx: ioctx.default_sampler_input(), input_evaluation=frozenset([]), output_creator=lambda ioctx: NoopOutput(), remote_worker_envs=False, remote_env_batch_wait_ms=0, soft_horizon=False, no_done_at_end=False, seed=None, extra_python_environs=None, fake_sampler=False): """Initialize a rollout worker. Arguments: env_creator (func): Function that returns a gym.Env given an EnvContext wrapped configuration. policy (class|dict): Either a class implementing Policy, or a dictionary of policy id strings to (Policy, obs_space, action_space, config) tuples. If a dict is specified, then we are in multi-agent mode and a policy_mapping_fn should also be set. policy_mapping_fn (func): A function that maps agent ids to policy ids in multi-agent mode. This function will be called each time a new agent appears in an episode, to bind that agent to a policy for the duration of the episode. policies_to_train (list): Optional whitelist of policies to train, or None for all policies. tf_session_creator (func): A function that returns a TF session. This is optional and only useful with TFPolicy. rollout_fragment_length (int): The target number of env transitions to include in each sample batch returned from this worker. batch_mode (str): One of the following batch modes: "truncate_episodes": Each call to sample() will return a batch of at most `rollout_fragment_length * num_envs` in size. The batch will be exactly `rollout_fragment_length * num_envs` in size if postprocessing does not change batch sizes. Episodes may be truncated in order to meet this size requirement. "complete_episodes": Each call to sample() will return a batch of at least `rollout_fragment_length * num_envs` in size. Episodes will not be truncated, but multiple episodes may be packed within one batch to meet the batch size. Note that when `num_envs > 1`, episode steps will be buffered until the episode completes, and hence batches may contain significant amounts of off-policy data. episode_horizon (int): Whether to stop episodes at this horizon. preprocessor_pref (str): Whether to prefer RLlib preprocessors ("rllib") or deepmind ("deepmind") when applicable. sample_async (bool): Whether to compute samples asynchronously in the background, which improves throughput but can cause samples to be slightly off-policy. compress_observations (bool): If true, compress the observations. They can be decompressed with rllib/utils/compression. num_envs (int): If more than one, will create multiple envs and vectorize the computation of actions. This has no effect if if the env already implements VectorEnv. observation_fn (ObservationFunction): Optional multi-agent observation function. observation_filter (str): Name of observation filter to use. clip_rewards (bool): Whether to clip rewards to [-1, 1] prior to experience postprocessing. Setting to None means clip for Atari only. clip_actions (bool): Whether to clip action values to the range specified by the policy action space. env_config (dict): Config to pass to the env creator. model_config (dict): Config to use when creating the policy model. policy_config (dict): Config to pass to the policy. In the multi-agent case, this config will be merged with the per-policy configs specified by `policy`. worker_index (int): For remote workers, this should be set to a non-zero and unique value. This index is passed to created envs through EnvContext so that envs can be configured per worker. num_workers (int): For remote workers, how many workers altogether have been created? monitor_path (str): Write out episode stats and videos to this directory if specified. log_dir (str): Directory where logs can be placed. log_level (str): Set the root log level on creation. callbacks (DefaultCallbacks): Custom training callbacks. input_creator (func): Function that returns an InputReader object for loading previous generated experiences. input_evaluation (list): How to evaluate the policy performance. This only makes sense to set when the input is reading offline data. The possible values include: - "is": the step-wise importance sampling estimator. - "wis": the weighted step-wise is estimator. - "simulation": run the environment in the background, but use this data for evaluation only and never for learning. output_creator (func): Function that returns an OutputWriter object for saving generated experiences. remote_worker_envs (bool): If using num_envs > 1, whether to create those new envs in remote processes instead of in the current process. This adds overheads, but can make sense if your envs remote_env_batch_wait_ms (float): Timeout that remote workers are waiting when polling environments. 0 (continue when at least one env is ready) is a reasonable default, but optimal value could be obtained by measuring your environment step / reset and model inference perf. 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. seed (int): Set the seed of both np and tf to this value to to ensure each remote worker has unique exploration behavior. extra_python_environs (dict): Extra python environments need to be set. fake_sampler (bool): Use a fake (inf speed) sampler for testing. """ self._original_kwargs = locals().copy() del self._original_kwargs["self"] global _global_worker _global_worker = self # set extra environs first if extra_python_environs: for key, value in extra_python_environs.items(): os.environ[key] = str(value) def gen_rollouts(): while True: yield self.sample() ParallelIteratorWorker.__init__(self, gen_rollouts, False) policy_config = policy_config or {} if (tf and policy_config.get("framework") == "tfe" and not policy_config.get("no_eager_on_workers") # This eager check is necessary for certain all-framework tests # that use tf's eager_mode() context generator. and not tf.executing_eagerly()): tf.enable_eager_execution() if log_level: logging.getLogger("ray.rllib").setLevel(log_level) if worker_index > 1: disable_log_once_globally() # only need 1 worker to log elif log_level == "DEBUG": enable_periodic_logging() env_context = EnvContext(env_config or {}, worker_index) self.policy_config = policy_config if callbacks: self.callbacks = callbacks() else: from ray.rllib.agents.callbacks import DefaultCallbacks self.callbacks = DefaultCallbacks() self.worker_index = worker_index self.num_workers = num_workers model_config = model_config or {} policy_mapping_fn = (policy_mapping_fn or (lambda agent_id: DEFAULT_POLICY_ID)) if not callable(policy_mapping_fn): raise ValueError("Policy mapping function not callable?") self.env_creator = env_creator self.rollout_fragment_length = rollout_fragment_length * num_envs self.batch_mode = batch_mode self.compress_observations = compress_observations self.preprocessing_enabled = True self.last_batch = None self.global_vars = None self.fake_sampler = fake_sampler self.env = _validate_env(env_creator(env_context)) if isinstance(self.env, (BaseEnv, MultiAgentEnv)): def wrap(env): return env # we can't auto-wrap these env types elif is_atari(self.env) and \ not model_config.get("custom_preprocessor") and \ preprocessor_pref == "deepmind": # Deepmind wrappers already handle all preprocessing self.preprocessing_enabled = False if clip_rewards is None: clip_rewards = True def wrap(env): env = wrap_deepmind( env, dim=model_config.get("dim"), framestack=model_config.get("framestack")) if monitor_path: from gym import wrappers env = wrappers.Monitor(env, monitor_path, resume=True) return env else: def wrap(env): if monitor_path: from gym import wrappers env = wrappers.Monitor(env, monitor_path, resume=True) return env self.env = wrap(self.env) def make_env(vector_index): return wrap( env_creator( env_context.copy_with_overrides( vector_index=vector_index, remote=remote_worker_envs))) self.tf_sess = None policy_dict = _validate_and_canonicalize(policy, self.env) self.policies_to_train = policies_to_train or list(policy_dict.keys()) # set numpy and python seed if seed is not None: np.random.seed(seed) random.seed(seed) if not hasattr(self.env, "seed"): raise ValueError("Env doesn't support env.seed(): {}".format( self.env)) self.env.seed(seed) try: assert torch is not None torch.manual_seed(seed) except AssertionError: logger.info("Could not seed torch") if _has_tensorflow_graph(policy_dict) and not (tf and tf.executing_eagerly()): if not tf: raise ImportError("Could not import tensorflow") with tf.Graph().as_default(): if tf_session_creator: self.tf_sess = tf_session_creator() else: self.tf_sess = tf.Session( config=tf.ConfigProto( gpu_options=tf.GPUOptions(allow_growth=True))) with self.tf_sess.as_default(): # set graph-level seed if seed is not None: tf.set_random_seed(seed) self.policy_map, self.preprocessors = \ self._build_policy_map(policy_dict, policy_config) if (ray.is_initialized() and ray.worker._mode() != ray.worker.LOCAL_MODE): if not ray.get_gpu_ids(): logger.debug( "Creating policy evaluation worker {}".format( worker_index) + " on CPU (please ignore any CUDA init errors)") elif not tf.test.is_gpu_available(): raise RuntimeError( "GPUs were assigned to this worker by Ray, but " "TensorFlow reports GPU acceleration is disabled. " "This could be due to a bad CUDA or TF installation.") else: self.policy_map, self.preprocessors = self._build_policy_map( policy_dict, policy_config) self.multiagent = set(self.policy_map.keys()) != {DEFAULT_POLICY_ID} if self.multiagent: if not ((isinstance(self.env, MultiAgentEnv) or isinstance(self.env, ExternalMultiAgentEnv)) or isinstance(self.env, BaseEnv)): raise ValueError( "Have multiple policies {}, but the env ".format( self.policy_map) + "{} is not a subclass of BaseEnv, MultiAgentEnv or " "ExternalMultiAgentEnv?".format(self.env)) self.filters = { policy_id: get_filter(observation_filter, policy.observation_space.shape) for (policy_id, policy) in self.policy_map.items() } if self.worker_index == 0: logger.info("Built filter map: {}".format(self.filters)) # Always use vector env for consistency even if num_envs = 1. self.async_env = BaseEnv.to_base_env( self.env, make_env=make_env, num_envs=num_envs, remote_envs=remote_worker_envs, remote_env_batch_wait_ms=remote_env_batch_wait_ms) self.num_envs = num_envs if self.batch_mode == "truncate_episodes": pack_episodes = True elif self.batch_mode == "complete_episodes": rollout_fragment_length = float("inf") # never cut episodes pack_episodes = False # sampler will return 1 episode per poll else: raise ValueError("Unsupported batch mode: {}".format( self.batch_mode)) self.io_context = IOContext(log_dir, policy_config, worker_index, self) self.reward_estimators = [] for method in input_evaluation: if method == "simulation": logger.warning( "Requested 'simulation' input evaluation method: " "will discard all sampler outputs and keep only metrics.") sample_async = True elif method == "is": ise = ImportanceSamplingEstimator.create(self.io_context) self.reward_estimators.append(ise) elif method == "wis": wise = WeightedImportanceSamplingEstimator.create( self.io_context) self.reward_estimators.append(wise) else: raise ValueError( "Unknown evaluation method: {}".format(method)) if sample_async: self.sampler = AsyncSampler( self, self.async_env, self.policy_map, policy_mapping_fn, self.preprocessors, self.filters, clip_rewards, rollout_fragment_length, self.callbacks, horizon=episode_horizon, pack=pack_episodes, tf_sess=self.tf_sess, clip_actions=clip_actions, blackhole_outputs="simulation" in input_evaluation, soft_horizon=soft_horizon, no_done_at_end=no_done_at_end, observation_fn=observation_fn) self.sampler.start() else: self.sampler = SyncSampler( self, self.async_env, self.policy_map, policy_mapping_fn, self.preprocessors, self.filters, clip_rewards, rollout_fragment_length, self.callbacks, horizon=episode_horizon, pack=pack_episodes, tf_sess=self.tf_sess, clip_actions=clip_actions, soft_horizon=soft_horizon, no_done_at_end=no_done_at_end, observation_fn=observation_fn) self.input_reader = input_creator(self.io_context) assert isinstance(self.input_reader, InputReader), self.input_reader self.output_writer = output_creator(self.io_context) assert isinstance(self.output_writer, OutputWriter), self.output_writer logger.debug( "Created rollout worker with env {} ({}), policies {}".format( self.async_env, self.env, self.policy_map))
[docs] @DeveloperAPI def sample(self): """Returns a batch of experience sampled from this worker. This method must be implemented by subclasses. Returns: SampleBatch|MultiAgentBatch: A columnar batch of experiences (e.g., tensors), or a multi-agent batch. Examples: >>> print(worker.sample()) SampleBatch({"obs": [1, 2, 3], "action": [0, 1, 0], ...}) """ if self.fake_sampler and self.last_batch is not None: return self.last_batch if log_once("sample_start"): logger.info("Generating sample batch of size {}".format( self.rollout_fragment_length)) batches = [self.input_reader.next()] steps_so_far = batches[0].count # In truncate_episodes mode, never pull more than 1 batch per env. # This avoids over-running the target batch size. if self.batch_mode == "truncate_episodes": max_batches = self.num_envs else: max_batches = float("inf") while (steps_so_far < self.rollout_fragment_length and len(batches) < max_batches): batch = self.input_reader.next() steps_so_far += batch.count batches.append(batch) batch = batches[0].concat_samples(batches) self.callbacks.on_sample_end(worker=self, samples=batch) # Always do writes prior to compression for consistency and to allow # for better compression inside the writer. self.output_writer.write(batch) # Do off-policy estimation if needed if self.reward_estimators: for sub_batch in batch.split_by_episode(): for estimator in self.reward_estimators: estimator.process(sub_batch) if log_once("sample_end"): logger.info("Completed sample batch:\n\n{}\n".format( summarize(batch))) if self.compress_observations == "bulk": batch.compress(bulk=True) elif self.compress_observations: batch.compress() if self.fake_sampler: self.last_batch = batch return batch
[docs] @DeveloperAPI @ray.method(num_return_vals=2) def sample_with_count(self): """Same as sample() but returns the count as a separate future.""" batch = self.sample() return batch, batch.count
[docs] @DeveloperAPI def get_weights(self, policies=None): """Returns the model weights of this worker. Returns: object: weights that can be set on another worker. info: dictionary of extra metadata. Examples: >>> weights = worker.get_weights() """ if policies is None: policies = self.policy_map.keys() return { pid: policy.get_weights() for pid, policy in self.policy_map.items() if pid in policies }
[docs] @DeveloperAPI def set_weights(self, weights, global_vars=None): """Sets the model weights of this worker. Examples: >>> weights = worker.get_weights() >>> worker.set_weights(weights) """ for pid, w in weights.items(): self.policy_map[pid].set_weights(w) if global_vars: self.set_global_vars(global_vars)
[docs] @DeveloperAPI def compute_gradients(self, samples): """Returns a gradient computed w.r.t the specified samples. Returns: (grads, info): A list of gradients that can be applied on a compatible worker. In the multi-agent case, returns a dict of gradients keyed by policy ids. An info dictionary of extra metadata is also returned. Examples: >>> batch = worker.sample() >>> grads, info = worker.compute_gradients(samples) """ if log_once("compute_gradients"): logger.info("Compute gradients on:\n\n{}\n".format( summarize(samples))) if isinstance(samples, MultiAgentBatch): grad_out, info_out = {}, {} if self.tf_sess is not None: builder = TFRunBuilder(self.tf_sess, "compute_gradients") for pid, batch in samples.policy_batches.items(): if pid not in self.policies_to_train: continue grad_out[pid], info_out[pid] = ( self.policy_map[pid]._build_compute_gradients( builder, batch)) grad_out = {k: builder.get(v) for k, v in grad_out.items()} info_out = {k: builder.get(v) for k, v in info_out.items()} else: for pid, batch in samples.policy_batches.items(): if pid not in self.policies_to_train: continue grad_out[pid], info_out[pid] = ( self.policy_map[pid].compute_gradients(batch)) else: grad_out, info_out = ( self.policy_map[DEFAULT_POLICY_ID].compute_gradients(samples)) info_out["batch_count"] = samples.count if log_once("grad_out"): logger.info("Compute grad info:\n\n{}\n".format( summarize(info_out))) return grad_out, info_out
[docs] @DeveloperAPI def apply_gradients(self, grads): """Applies the given gradients to this worker's weights. Examples: >>> samples = worker.sample() >>> grads, info = worker.compute_gradients(samples) >>> worker.apply_gradients(grads) """ if log_once("apply_gradients"): logger.info("Apply gradients:\n\n{}\n".format(summarize(grads))) if isinstance(grads, dict): if self.tf_sess is not None: builder = TFRunBuilder(self.tf_sess, "apply_gradients") outputs = { pid: self.policy_map[pid]._build_apply_gradients( builder, grad) for pid, grad in grads.items() } return {k: builder.get(v) for k, v in outputs.items()} else: return { pid: self.policy_map[pid].apply_gradients(g) for pid, g in grads.items() } else: return self.policy_map[DEFAULT_POLICY_ID].apply_gradients(grads)
[docs] @DeveloperAPI def learn_on_batch(self, samples): """Update policies based on the given batch. This is the equivalent to apply_gradients(compute_gradients(samples)), but can be optimized to avoid pulling gradients into CPU memory. Returns: info: dictionary of extra metadata from compute_gradients(). Examples: >>> batch = worker.sample() >>> worker.learn_on_batch(samples) """ if log_once("learn_on_batch"): logger.info( "Training on concatenated sample batches:\n\n{}\n".format( summarize(samples))) if isinstance(samples, MultiAgentBatch): info_out = {} to_fetch = {} if self.tf_sess is not None: builder = TFRunBuilder(self.tf_sess, "learn_on_batch") else: builder = None for pid, batch in samples.policy_batches.items(): if pid not in self.policies_to_train: continue policy = self.policy_map[pid] if builder and hasattr(policy, "_build_learn_on_batch"): to_fetch[pid] = policy._build_learn_on_batch( builder, batch) else: info_out[pid] = policy.learn_on_batch(batch) info_out.update({k: builder.get(v) for k, v in to_fetch.items()}) else: info_out = { DEFAULT_POLICY_ID: self.policy_map[DEFAULT_POLICY_ID] .learn_on_batch(samples) } if log_once("learn_out"): logger.debug("Training out:\n\n{}\n".format(summarize(info_out))) return info_out
[docs] def sample_and_learn(self, expected_batch_size, num_sgd_iter, sgd_minibatch_size, standardize_fields): """Sample and batch and learn on it. This is typically used in combination with distributed allreduce. Arguments: expected_batch_size (int): Expected number of samples to learn on. num_sgd_iter (int): Number of SGD iterations. sgd_minibatch_size (int): SGD minibatch size. standardize_fields (list): List of sample fields to normalize. Returns: info: dictionary of extra metadata from learn_on_batch(). count: number of samples learned on. """ batch = self.sample() assert batch.count == expected_batch_size, \ ("Batch size possibly out of sync between workers, expected:", expected_batch_size, "got:", batch.count) logger.info("Executing distributed minibatch SGD " "with epoch size {}, minibatch size {}".format( batch.count, sgd_minibatch_size)) info = do_minibatch_sgd(batch, self.policy_map, self, num_sgd_iter, sgd_minibatch_size, standardize_fields) return info, batch.count
[docs] @DeveloperAPI def get_metrics(self): """Returns a list of new RolloutMetric objects from evaluation.""" out = self.sampler.get_metrics() for m in self.reward_estimators: out.extend(m.get_metrics()) return out
[docs] @DeveloperAPI def foreach_env(self, func): """Apply the given function to each underlying env instance.""" envs = self.async_env.get_unwrapped() if not envs: return [func(self.async_env)] else: return [func(e) for e in envs]
[docs] @DeveloperAPI def get_policy(self, policy_id=DEFAULT_POLICY_ID): """Return policy for the specified id, or None. Arguments: policy_id (str): id of policy to return. """ return self.policy_map.get(policy_id)
[docs] @DeveloperAPI def for_policy(self, func, policy_id=DEFAULT_POLICY_ID): """Apply the given function to the specified policy.""" return func(self.policy_map[policy_id])
[docs] @DeveloperAPI def foreach_policy(self, func): """Apply the given function to each (policy, policy_id) tuple.""" return [func(policy, pid) for pid, policy in self.policy_map.items()]
[docs] @DeveloperAPI def foreach_trainable_policy(self, func): """ Applies the given function to each (policy, policy_id) tuple, which can be found in `self.policies_to_train`. Args: func (callable): A function - taking a Policy and its ID - that is called on all Policies within `self.policies_to_train`. Returns: List[any]: The list of n return values of all `func([policy], [ID])`-calls. """ return [ func(policy, pid) for pid, policy in self.policy_map.items() if pid in self.policies_to_train ]
[docs] @DeveloperAPI def sync_filters(self, new_filters): """Changes self's filter to given and rebases any accumulated delta. Args: new_filters (dict): Filters with new state to update local copy. """ assert all(k in new_filters for k in self.filters) for k in self.filters: self.filters[k].sync(new_filters[k])
[docs] @DeveloperAPI def get_filters(self, flush_after=False): """Returns a snapshot of filters. Args: flush_after (bool): Clears the filter buffer state. Returns: return_filters (dict): Dict for serializable filters """ return_filters = {} for k, f in self.filters.items(): return_filters[k] = f.as_serializable() if flush_after: f.clear_buffer() return return_filters
@DeveloperAPI def save(self): filters = self.get_filters(flush_after=True) state = { pid: self.policy_map[pid].get_state() for pid in self.policy_map } return pickle.dumps({"filters": filters, "state": state}) @DeveloperAPI def restore(self, objs): objs = pickle.loads(objs) self.sync_filters(objs["filters"]) for pid, state in objs["state"].items(): self.policy_map[pid].set_state(state) @DeveloperAPI def set_global_vars(self, global_vars): self.foreach_policy(lambda p, _: p.on_global_var_update(global_vars)) self.global_vars = global_vars @DeveloperAPI def get_global_vars(self): return self.global_vars @DeveloperAPI def export_policy_model(self, export_dir, policy_id=DEFAULT_POLICY_ID): self.policy_map[policy_id].export_model(export_dir) @DeveloperAPI def import_policy_model_from_h5(self, import_file, policy_id=DEFAULT_POLICY_ID): self.policy_map[policy_id].import_model_from_h5(import_file) @DeveloperAPI def export_policy_checkpoint(self, export_dir, filename_prefix="model", policy_id=DEFAULT_POLICY_ID): self.policy_map[policy_id].export_checkpoint(export_dir, filename_prefix) @DeveloperAPI def stop(self): self.async_env.stop()
[docs] @DeveloperAPI def creation_args(self): """Returns the args used to create this worker.""" return self._original_kwargs
[docs] @DeveloperAPI def get_host(self): """Returns the hostname of the process running this evaluator.""" return os.uname()[1]
[docs] @DeveloperAPI def apply(self, func, *args): """Apply the given function to this evaluator instance.""" return func(self, *args)
def _build_policy_map(self, policy_dict, policy_config): policy_map = {} preprocessors = {} for name, (cls, obs_space, act_space, conf) in sorted(policy_dict.items()): logger.debug("Creating policy for {}".format(name)) merged_conf = merge_dicts(policy_config, conf) merged_conf["num_workers"] = self.num_workers merged_conf["worker_index"] = self.worker_index if self.preprocessing_enabled: preprocessor = ModelCatalog.get_preprocessor_for_space( obs_space, merged_conf.get("model")) preprocessors[name] = preprocessor obs_space = preprocessor.observation_space else: preprocessors[name] = NoPreprocessor(obs_space) if isinstance(obs_space, gym.spaces.Dict) or \ isinstance(obs_space, gym.spaces.Tuple): raise ValueError( "Found raw Tuple|Dict space as input to policy. " "Please preprocess these observations with a " "Tuple|DictFlatteningPreprocessor.") if tf and tf.executing_eagerly(): if hasattr(cls, "as_eager"): cls = cls.as_eager() if policy_config["eager_tracing"]: cls = cls.with_tracing() elif not issubclass(cls, TFPolicy): pass # could be some other type of policy else: raise ValueError("This policy does not support eager " "execution: {}".format(cls)) if tf: with tf.variable_scope(name): policy_map[name] = cls(obs_space, act_space, merged_conf) else: policy_map[name] = cls(obs_space, act_space, merged_conf) if self.worker_index == 0: logger.info("Built policy map: {}".format(policy_map)) logger.info("Built preprocessor map: {}".format(preprocessors)) return policy_map, preprocessors
[docs] def setup_torch_data_parallel(self, url, world_rank, world_size, backend): """Join a torch process group for distributed SGD.""" logger.info("Joining process group, url={}, world_rank={}, " "world_size={}, backend={}".format(url, world_rank, world_size, backend)) torch.distributed.init_process_group( backend=backend, init_method=url, rank=world_rank, world_size=world_size) for pid, policy in self.policy_map.items(): if not isinstance(policy, TorchPolicy): raise ValueError( "This policy does not support torch distributed", policy) policy.distributed_world_size = world_size
[docs] def get_node_ip(self): """Returns the IP address of the current node.""" return ray.services.get_node_ip_address()
[docs] def find_free_port(self): """Finds a free port on the current node.""" from ray.util.sgd import utils return utils.find_free_port()
def __del__(self): if hasattr(self, "sampler") and isinstance(self.sampler, AsyncSampler): self.sampler.shutdown = True
def _validate_and_canonicalize(policy, env): if isinstance(policy, dict): _validate_multiagent_config(policy) return policy elif not issubclass(policy, Policy): raise ValueError("policy must be a rllib.Policy class") else: if (isinstance(env, MultiAgentEnv) and not hasattr(env, "observation_space")): raise ValueError( "MultiAgentEnv must have observation_space defined if run " "in a single-agent configuration.") return { DEFAULT_POLICY_ID: (policy, env.observation_space, env.action_space, {}) } def _validate_multiagent_config(policy, allow_none_graph=False): for k, v in policy.items(): if not isinstance(k, str): raise ValueError("policy keys must be strs, got {}".format( type(k))) if not isinstance(v, (tuple, list)) or len(v) != 4: raise ValueError( "policy values must be tuples/lists of " "(cls or None, obs_space, action_space, config), got {}". format(v)) if allow_none_graph and v[0] is None: pass elif not issubclass(v[0], Policy): raise ValueError("policy tuple value 0 must be a rllib.Policy " "class or None, got {}".format(v[0])) if not isinstance(v[1], gym.Space): raise ValueError( "policy tuple value 1 (observation_space) must be a " "gym.Space, got {}".format(type(v[1]))) if not isinstance(v[2], gym.Space): raise ValueError("policy tuple value 2 (action_space) must be a " "gym.Space, got {}".format(type(v[2]))) if not isinstance(v[3], dict): raise ValueError("policy tuple value 3 (config) must be a dict, " "got {}".format(type(v[3]))) def _validate_env(env): # allow this as a special case (assumed gym.Env) if hasattr(env, "observation_space") and hasattr(env, "action_space"): return env allowed_types = [gym.Env, MultiAgentEnv, ExternalEnv, VectorEnv, BaseEnv] if not any(isinstance(env, tpe) for tpe in allowed_types): raise ValueError( "Returned env should be an instance of gym.Env, MultiAgentEnv, " "ExternalEnv, VectorEnv, or BaseEnv. The provided env creator " "function returned {} ({}).".format(env, type(env))) return env def _has_tensorflow_graph(policy_dict): for policy, _, _, _ in policy_dict.values(): if issubclass(policy, TFPolicy): return True return False