def setup(self, config: PartialTrainerConfigDict):
super().setup(config)
# Shortcut: If execution_plan, thread and buffer will be created in there.
if self.config["_disable_execution_plan_api"] is False:
return
# Tag those workers (top 1/3rd indices) that we should collect episodes from
# for metrics due to `PerWorkerEpsilonGreedy` exploration strategy.
if self.workers.remote_workers():
self._remote_workers_for_metrics = self.workers.remote_workers(
)[-len(self.workers.remote_workers()) // 3:]
num_replay_buffer_shards = self.config["optimizer"][
"num_replay_buffer_shards"]
buffer_size = (self.config["replay_buffer_config"]["capacity"] //
num_replay_buffer_shards)
replay_actor_args = [
num_replay_buffer_shards,
self.config["learning_starts"],
buffer_size,
self.config["train_batch_size"],
self.config["replay_buffer_config"]["prioritized_replay_alpha"],
self.config["replay_buffer_config"]["prioritized_replay_beta"],
self.config["replay_buffer_config"]["prioritized_replay_eps"],
self.config["multiagent"]["replay_mode"],
self.config["replay_buffer_config"].get("replay_sequence_length",
1),
]
# Place all replay buffer shards on the same node as the learner
# (driver process that runs this execution plan).
if self.config["replay_buffer_shards_colocated_with_driver"]:
self.replay_actors = create_colocated_actors(
actor_specs=[ # (class, args, kwargs={}, count)
(ReplayActor, replay_actor_args, {},
num_replay_buffer_shards)
],
node=platform.node(), # localhost
)[0] # [0]=only one item in `actor_specs`.
# Place replay buffer shards on any node(s).
else:
self.replay_actors = [
ReplayActor.remote(*replay_actor_args)
for _ in range(num_replay_buffer_shards)
]
self.learner_thread = LearnerThread(self.workers.local_worker())
self.learner_thread.start()
self.steps_since_update = defaultdict(int)
weights = self.workers.local_worker().get_weights()
self.curr_learner_weights = ray.put(weights)
self.remote_sampling_requests_in_flight: DefaultDict[
ActorHandle, Set[ray.ObjectRef]] = defaultdict(set)
self.remote_replay_requests_in_flight: DefaultDict[
ActorHandle, Set[ray.ObjectRef]] = defaultdict(set)
self.curr_num_samples_collected = 0
self.replay_sample_batches = []
self._num_ts_trained_since_last_target_update = 0
def test_store_to_replay_actor(ray_start_regular_shared):
actor = ReplayActor.remote(num_shards=1,
learning_starts=200,
buffer_size=1000,
replay_batch_size=100,
prioritized_replay_alpha=0.6,
prioritized_replay_beta=0.4,
prioritized_replay_eps=0.0001)
assert ray.get(actor.replay.remote()) is None
workers = make_workers(0)
a = ParallelRollouts(workers, mode="bulk_sync")
b = a.for_each(StoreToReplayBuffer(actors=[actor]))
next(b)
assert ray.get(actor.replay.remote()) is None # learning hasn't started
next(b)
assert ray.get(actor.replay.remote()).count == 100
replay_op = Replay(actors=[actor])
assert next(replay_op).count == 100
def execution_plan(workers: WorkerSet, config: dict,
**kwargs) -> LocalIterator[dict]:
assert (
len(kwargs) == 0
), "Apex execution_plan does NOT take any additional parameters"
# Create a number of replay buffer actors.
num_replay_buffer_shards = config["optimizer"][
"num_replay_buffer_shards"]
buffer_size = (config["replay_buffer_config"]["capacity"] //
num_replay_buffer_shards)
replay_actor_args = [
num_replay_buffer_shards,
config["learning_starts"],
buffer_size,
config["train_batch_size"],
config["replay_buffer_config"]["prioritized_replay_alpha"],
config["replay_buffer_config"]["prioritized_replay_beta"],
config["replay_buffer_config"]["prioritized_replay_eps"],
config["multiagent"]["replay_mode"],
config["replay_buffer_config"].get("replay_sequence_length", 1),
]
# Place all replay buffer shards on the same node as the learner
# (driver process that runs this execution plan).
if config["replay_buffer_shards_colocated_with_driver"]:
replay_actors = create_colocated_actors(
actor_specs=[
# (class, args, kwargs={}, count)
(ReplayActor, replay_actor_args, {},
num_replay_buffer_shards)
],
node=platform.node(), # localhost
)[0] # [0]=only one item in `actor_specs`.
# Place replay buffer shards on any node(s).
else:
replay_actors = [
ReplayActor(*replay_actor_args)
for _ in range(num_replay_buffer_shards)
]
# Start the learner thread.
learner_thread = LearnerThread(workers.local_worker())
learner_thread.start()
# Update experience priorities post learning.
def update_prio_and_stats(
item: Tuple[ActorHandle, dict, int, int]) -> None:
actor, prio_dict, env_count, agent_count = item
if config.get("prioritized_replay"):
actor.update_priorities.remote(prio_dict)
metrics = _get_shared_metrics()
# Manually update the steps trained counter since the learner
# thread is executing outside the pipeline.
metrics.counters[STEPS_TRAINED_THIS_ITER_COUNTER] = env_count
metrics.counters[STEPS_TRAINED_COUNTER] += env_count
metrics.timers["learner_dequeue"] = learner_thread.queue_timer
metrics.timers["learner_grad"] = learner_thread.grad_timer
metrics.timers["learner_overall"] = learner_thread.overall_timer
# We execute the following steps concurrently:
# (1) Generate rollouts and store them in one of our replay buffer
# actors. Update the weights of the worker that generated the batch.
rollouts = ParallelRollouts(workers, mode="async", num_async=2)
store_op = rollouts.for_each(StoreToReplayBuffer(actors=replay_actors))
# Only need to update workers if there are remote workers.
if workers.remote_workers():
store_op = store_op.zip_with_source_actor().for_each(
UpdateWorkerWeights(
learner_thread,
workers,
max_weight_sync_delay=(
config["optimizer"]["max_weight_sync_delay"]),
))
# (2) Read experiences from one of the replay buffer actors and send
# to the learner thread via its in-queue.
post_fn = config.get("before_learn_on_batch") or (lambda b, *a: b)
replay_op = (Replay(
actors=replay_actors, num_async=4).for_each(lambda x: post_fn(
x, workers, config)).zip_with_source_actor().for_each(
Enqueue(learner_thread.inqueue)))
# (3) Get priorities back from learner thread and apply them to the
# replay buffer actors.
update_op = (Dequeue(learner_thread.outqueue,
check=learner_thread.is_alive).for_each(
update_prio_and_stats).for_each(
UpdateTargetNetwork(
workers,
config["target_network_update_freq"],
by_steps_trained=True)))
if config["training_intensity"]:
# Execute (1), (2) with a fixed intensity ratio.
rr_weights = calculate_rr_weights(config) + ["*"]
merged_op = Concurrently(
[store_op, replay_op, update_op],
mode="round_robin",
output_indexes=[2],
round_robin_weights=rr_weights,
)
else:
# Execute (1), (2), (3) asynchronously as fast as possible. Only
# output items from (3) since metrics aren't available before
# then.
merged_op = Concurrently([store_op, replay_op, update_op],
mode="async",
output_indexes=[2])
# Add in extra replay and learner metrics to the training result.
def add_apex_metrics(result: dict) -> dict:
replay_stats = ray.get(replay_actors[0].stats.remote(
config["optimizer"].get("debug")))
exploration_infos = workers.foreach_policy_to_train(
lambda p, _: p.get_exploration_state())
result["info"].update({
"exploration_infos":
exploration_infos,
"learner_queue":
learner_thread.learner_queue_size.stats(),
LEARNER_INFO:
copy.deepcopy(learner_thread.learner_info),
"replay_shard_0":
replay_stats,
})
return result
# Only report metrics from the workers with the lowest 1/3 of
# epsilons.
selected_workers = workers.remote_workers(
)[-len(workers.remote_workers()) // 3:]
return StandardMetricsReporting(
merged_op, workers, config,
selected_workers=selected_workers).for_each(add_apex_metrics)