class ActorWorker(object):
def __init__(self, local_actor: AbsActor, proxy_params):
self._local_actor = local_actor
self._proxy = Proxy(component_type="actor_worker", **proxy_params)
self._registry_table = RegisterTable(self._proxy.get_peers)
self._registry_table.register_event_handler("actor:rollout:1", self.on_rollout_request)
def on_rollout_request(self, message):
data = message.payload
if data.get(PayloadKey.DONE, False):
sys.exit(0)
performance, experiences = self._local_actor.roll_out(model_dict=data[PayloadKey.MODEL],
epsilon_dict=data[PayloadKey.EPSILON],
return_details=data[PayloadKey.RETURN_DETAILS])
self._proxy.reply(received_message=message,
tag=MessageTag.UPDATE,
payload={PayloadKey.PERFORMANCE: performance,
PayloadKey.EXPERIENCE: experiences}
)
def launch(self):
"""
This launches an ActorWorker instance.
"""
for msg in self._proxy.receive():
self._registry_table.push(msg)
triggered_events = self._registry_table.get()
for handler_fn, cached_messages in triggered_events:
handler_fn(cached_messages)
def actor_init(queue, redis_port):
proxy = Proxy(component_type="actor",
expected_peers={"master": 1},
redis_address=("localhost", redis_port),
**PROXY_PARAMETER)
# Continuously receive messages from proxy.
for msg in proxy.receive(is_continuous=True):
print(f"receive message from master. {msg.tag}")
if msg.tag == "cont":
proxy.reply(message=msg, tag="recv", payload="successful receive!")
elif msg.tag == "stop":
proxy.reply(message=msg,
tag="recv",
payload=f"{proxy.name} exited!")
queue.put(proxy.name)
break
elif msg.tag == "finish":
proxy.reply(message=msg,
tag="recv",
payload=f"{proxy.name} finish!")
sys.exit(0)
proxy.close()
sys.exit(1)
class ActorWorker(object):
"""A ``AbsActor`` wrapper that accepts roll-out requests and performs roll-out tasks.
Args:
local_actor: An ``AbsActor`` instance.
proxy_params: Parameters for instantiating a ``Proxy`` instance.
"""
def __init__(self, local_actor: AbsActor, proxy_params):
self._local_actor = local_actor
self._proxy = Proxy(component_type="actor_worker", **proxy_params)
self._registry_table = RegisterTable(self._proxy.get_peers)
self._registry_table.register_event_handler("actor:rollout:1",
self.on_rollout_request)
def on_rollout_request(self, message):
"""Perform local roll-out and send the results back to the request sender.
Args:
message: Message containing roll-out parameters and options.
"""
data = message.payload
if data.get(PayloadKey.DONE, False):
sys.exit(0)
performance, experiences = self._local_actor.roll_out(
model_dict=data[PayloadKey.MODEL],
epsilon_dict=data[PayloadKey.EPSILON],
return_details=data[PayloadKey.RETURN_DETAILS])
self._proxy.reply(received_message=message,
tag=MessageTag.UPDATE,
payload={
PayloadKey.PERFORMANCE: performance,
PayloadKey.EXPERIENCE: experiences
})
def launch(self):
"""Entry point method.
This enters the actor into an infinite loop of listening to requests and handling them according to the
register table. In this case, the only type of requests the actor needs to handle is roll-out requests.
"""
for msg in self._proxy.receive():
self._registry_table.push(msg)
triggered_events = self._registry_table.get()
for handler_fn, cached_messages in triggered_events:
handler_fn(cached_messages)
def worker(group_name):
"""
The main worker logic includes initialize proxy and handle jobs from the master.
Args:
group_name (str): Identifier for the group of all communication components
"""
proxy = Proxy(group_name=group_name,
component_type="worker",
expected_peers={"master": 1})
# Nonrecurring receive the message from the proxy.
for msg in proxy.receive(is_continuous=False):
print(f"{proxy.component_name} receive message from {msg.source}. the payload is {msg.payload}.")
if msg.tag == "sum":
replied_payload = sum(msg.payload)
proxy.reply(received_message=msg, tag="sum", payload=replied_payload)
def as_worker(self, group: str, proxy_options=None, log_dir: str = getcwd()):
"""Executes an event loop where roll-outs are performed on demand from a remote learner.
Args:
group (str): Identifier of the group to which the actor belongs. It must be the same group name
assigned to the learner (and decision clients, if any).
proxy_options (dict): Keyword parameters for the internal ``Proxy`` instance. See ``Proxy`` class
for details. Defaults to None.
"""
if proxy_options is None:
proxy_options = {}
proxy = Proxy(group, "actor", {"learner": 1}, **proxy_options)
logger = Logger(proxy.name, dump_folder=log_dir)
for msg in proxy.receive():
if msg.tag == MessageTag.EXIT:
logger.info("Exiting...")
proxy.close()
sys.exit(0)
elif msg.tag == MessageTag.ROLLOUT:
ep = msg.payload[PayloadKey.ROLLOUT_INDEX]
logger.info(f"Rolling out ({ep})...")
metrics, rollout_data = self.roll_out(
ep,
training=msg.payload[PayloadKey.TRAINING],
model_by_agent=msg.payload[PayloadKey.MODEL],
exploration_params=msg.payload[PayloadKey.EXPLORATION_PARAMS]
)
if rollout_data is None:
logger.info(f"Roll-out {ep} aborted")
else:
logger.info(f"Roll-out {ep} finished")
rollout_finish_msg = Message(
MessageTag.FINISHED,
proxy.name,
proxy.peers_name["learner"][0],
payload={
PayloadKey.ROLLOUT_INDEX: ep,
PayloadKey.METRICS: metrics,
PayloadKey.DETAILS: rollout_data
}
)
proxy.isend(rollout_finish_msg)
self.env.reset()
def worker(group_name):
"""
The main worker logic includes initialize proxy and handle jobs from the master.
Args:
group_name (str): Identifier for the group of all communication components.
"""
proxy = Proxy(group_name=group_name,
component_type="worker",
expected_peers={"master": 1})
counter = 0
print(f"{proxy.component_name}'s counter is {counter}.")
# nonrecurring receive the message from the proxy.
for msg in proxy.receive(is_continuous=False):
print(f"{proxy.component_name} receive message from {msg.source}.")
if msg.tag == "INC":
counter += 1
print(
f"{proxy.component_name} receive INC request, {proxy.component_name}'s count is {counter}."
)
proxy.reply(received_message=msg, tag="done")
class ActorWorker(object):
def __init__(self, local_actor, proxy_params):
self._local_actor = local_actor
self._proxy = Proxy(component_type="actor_worker", **proxy_params)
self._registry_table = RegisterTable(self._proxy.get_peers)
self._registry_table.register_event_handler("actor_proxy:rollout:1",
self.on_rollout_request)
def on_rollout_request(self, message):
if message.payload[PayloadKey.RolloutMode] == RolloutMode.EXIT:
sys.exit(0)
data = message.payload
performance, exp_by_agent = self._local_actor.roll_out(
mode=data[PayloadKey.RolloutMode],
models=data[PayloadKey.MODEL],
epsilon_dict=data[PayloadKey.EPSILON],
seed=data[PayloadKey.SEED])
self._proxy.reply(received_message=message,
tag=MessageType.UPDATE,
payload={
PayloadKey.PERFORMANCE: performance["local"],
PayloadKey.EXPERIENCE: exp_by_agent
})
def launch(self):
# TODO
"""
Launches the an ActorWorker instance
"""
for msg in self._proxy.receive():
self._registry_table.push(msg)
triggered_events = self._registry_table.get()
for handler_fn, cached_messages in triggered_events:
handler_fn(cached_messages)
class ActorProxy(object):
"""Actor proxy that manages a set of remote actors.
Args:
group_name (str): Identifier of the group to which the actor belongs. It must be the same group name
assigned to the actors (and roll-out clients, if any).
num_actors (int): Expected number of actors in the group identified by ``group_name``.
update_trigger (str): Number or percentage of ``MessageTag.FINISHED`` messages required to trigger
learner updates, i.e., model training.
proxy_options (dict): Keyword parameters for the internal ``Proxy`` instance. See ``Proxy`` class
for details. Defaults to None.
"""
def __init__(
self,
group_name: str,
num_actors: int,
update_trigger: str = None,
proxy_options: dict = None
):
self.agent = None
peers = {"actor": num_actors}
if proxy_options is None:
proxy_options = {}
self._proxy = Proxy(group_name, "learner", peers, **proxy_options)
self._actors = self._proxy.peers_name["actor"] # remote actor ID's
self._registry_table = RegisterTable(self._proxy.peers_name)
if update_trigger is None:
update_trigger = len(self._actors)
self._registry_table.register_event_handler(
f"actor:{MessageTag.FINISHED.value}:{update_trigger}", self._on_rollout_finish
)
self.logger = InternalLogger("ACTOR_PROXY")
def roll_out(self, index: int, training: bool = True, model_by_agent: dict = None, exploration_params=None):
"""Collect roll-out data from remote actors.
Args:
index (int): Index of roll-out requests.
training (bool): If true, the roll-out request is for training purposes.
model_by_agent (dict): Models to be broadcast to remote actors for inference. Defaults to None.
exploration_params: Exploration parameters to be used by the remote roll-out actors. Defaults to None.
"""
payload = {
PayloadKey.ROLLOUT_INDEX: index,
PayloadKey.TRAINING: training,
PayloadKey.MODEL: model_by_agent,
PayloadKey.EXPLORATION_PARAMS: exploration_params
}
self._proxy.iscatter(MessageTag.ROLLOUT, SessionType.TASK, [(actor, payload) for actor in self._actors])
self.logger.info(f"Sent roll-out requests to {self._actors} for ep-{index}")
# Receive roll-out results from remote actors
for msg in self._proxy.receive():
if msg.payload[PayloadKey.ROLLOUT_INDEX] != index:
self.logger.info(
f"Ignore a message of type {msg.tag} with ep {msg.payload[PayloadKey.ROLLOUT_INDEX]} "
f"(expected {index} or greater)"
)
continue
if msg.tag == MessageTag.FINISHED:
# If enough update messages have been received, call update() and break out of the loop to start
# the next episode.
result = self._registry_table.push(msg)
if result:
env_metrics, details = result[0]
break
return env_metrics, details
def _on_rollout_finish(self, messages: List[Message]):
metrics = {msg.source: msg.payload[PayloadKey.METRICS] for msg in messages}
details = {msg.source: msg.payload[PayloadKey.DETAILS] for msg in messages}
return metrics, details
def terminate(self):
"""Tell the remote actors to exit."""
self._proxy.ibroadcast(
component_type="actor", tag=MessageTag.EXIT, session_type=SessionType.NOTIFICATION
)
self.logger.info("Exiting...")
