Source code for ray.rllib.env.policy_server_input

import logging
import queue
import threading
import traceback

from http.server import SimpleHTTPRequestHandler, HTTPServer
from socketserver import ThreadingMixIn

import ray.cloudpickle as pickle
from ray.rllib.offline.input_reader import InputReader
from ray.rllib.env.policy_client import PolicyClient, \
from ray.rllib.utils.annotations import override, PublicAPI

logger = logging.getLogger(__name__)

[docs]class PolicyServerInput(ThreadingMixIn, HTTPServer, InputReader): """REST policy server that acts as an offline data source. This launches a multi-threaded server that listens on the specified host and port to serve policy requests and forward experiences to RLlib. For high performance experience collection, it implements InputReader. For an example, run `examples/` along with `examples/ --inference-mode=local|remote`. Examples: >>> pg = PGTrainer( ... env="CartPole-v0", config={ ... "input": lambda ioctx: ... PolicyServerInput(ioctx, addr, port), ... "num_workers": 0, # Run just 1 server, in the trainer. ... } >>> while True: >>> pg.train() >>> client = PolicyClient("localhost:9900", inference_mode="local") >>> eps_id = client.start_episode() >>> action = client.get_action(eps_id, obs) >>> ... >>> client.log_returns(eps_id, reward) >>> ... >>> client.log_returns(eps_id, reward) """ @PublicAPI def __init__(self, ioctx, address, port): """Create a PolicyServerInput. This class implements rllib.offline.InputReader, and can be used with any Trainer by configuring {"num_workers": 0, "input": lambda ioctx: PolicyServerInput(ioctx, addr, port)} Note that by setting num_workers: 0, the trainer will only create one rollout worker / PolicyServerInput. Clients can connect to the launched server using rllib.env.PolicyClient. Args: ioctx (IOContext): IOContext provided by RLlib. address (str): Server addr (e.g., "localhost"). port (int): Server port (e.g., 9900). """ self.rollout_worker = ioctx.worker self.samples_queue = queue.Queue() self.metrics_queue = queue.Queue() def get_metrics(): completed = [] while True: try: completed.append(self.metrics_queue.get_nowait()) except queue.Empty: break return completed # Forwards client-reported rewards directly into the local rollout # worker. This is a bit of a hack since it is patching the get_metrics # function of the sampler. self.rollout_worker.sampler.get_metrics = get_metrics handler = _make_handler(self.rollout_worker, self.samples_queue, self.metrics_queue) HTTPServer.__init__(self, (address, port), handler)"")"Starting connector server at {}:{}".format(address, port))"") thread = threading.Thread(name="server", target=self.serve_forever) thread.daemon = True thread.start()
[docs] @override(InputReader) def next(self): return self.samples_queue.get()
def _make_handler(rollout_worker, samples_queue, metrics_queue): # Only used in remote inference mode. We must create a new rollout worker # then since the original worker doesn't have the env properly wrapped in # an ExternalEnv interface. child_rollout_worker = None inference_thread = None lock = threading.Lock() def setup_child_rollout_worker(): nonlocal lock nonlocal child_rollout_worker nonlocal inference_thread with lock: if child_rollout_worker is None: (child_rollout_worker, inference_thread) = _create_embedded_rollout_worker( rollout_worker.creation_args(), report_data) child_rollout_worker.set_weights(rollout_worker.get_weights()) def report_data(data): nonlocal child_rollout_worker batch = data["samples"] batch.decompress_if_needed() samples_queue.put(batch) for rollout_metric in data["metrics"]: metrics_queue.put(rollout_metric) if child_rollout_worker is not None: child_rollout_worker.set_weights(rollout_worker.get_weights(), rollout_worker.get_global_vars()) class Handler(SimpleHTTPRequestHandler): def __init__(self, *a, **kw): super().__init__(*a, **kw) def do_POST(self): content_len = int(self.headers.get("Content-Length"), 0) raw_body = parsed_input = pickle.loads(raw_body) try: response = self.execute_command(parsed_input) self.send_response(200) self.end_headers() self.wfile.write(pickle.dumps(response)) except Exception: self.send_error(500, traceback.format_exc()) def execute_command(self, args): command = args["command"] response = {} # Local inference commands: if command == PolicyClient.GET_WORKER_ARGS:"Sending worker creation args to client.") response["worker_args"] = rollout_worker.creation_args() elif command == PolicyClient.GET_WEIGHTS:"Sending worker weights to client.") response["weights"] = rollout_worker.get_weights() response["global_vars"] = rollout_worker.get_global_vars() elif command == PolicyClient.REPORT_SAMPLES:"Got sample batch of size {} from client.".format( args["samples"].count)) report_data(args) # Remote inference commands: elif command == PolicyClient.START_EPISODE: setup_child_rollout_worker() assert inference_thread.is_alive() response["episode_id"] = ( child_rollout_worker.env.start_episode( args["episode_id"], args["training_enabled"])) elif command == PolicyClient.GET_ACTION: assert inference_thread.is_alive() response["action"] = child_rollout_worker.env.get_action( args["episode_id"], args["observation"]) elif command == PolicyClient.LOG_ACTION: assert inference_thread.is_alive() child_rollout_worker.env.log_action( args["episode_id"], args["observation"], args["action"]) elif command == PolicyClient.LOG_RETURNS: assert inference_thread.is_alive() if args["done"]: child_rollout_worker.env.log_returns( args["episode_id"], args["reward"], args["info"], args["done"]) else: child_rollout_worker.env.log_returns( args["episode_id"], args["reward"], args["info"]) elif command == PolicyClient.END_EPISODE: assert inference_thread.is_alive() child_rollout_worker.env.end_episode(args["episode_id"], args["observation"]) else: raise ValueError("Unknown command: {}".format(command)) return response return Handler