import rl_main.utils as utils
os.environ["CUDA_VISIBLE_DEVICES"] = CUDA_VISIBLE_DEVICES_NUMBER_LIST
if __name__ == "__main__":
torch.manual_seed(SEED)
if torch.cuda.is_available():
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
utils.make_output_folders()
utils.ask_file_removal()
env = rl_utils.get_environment()
rl_model = rl_utils.get_rl_model(env, -1)
utils.print_configuration(env, rl_model)
try:
chief = Process(target=utils.run_chief, args=())
chief.start()
time.sleep(1.5)
workers = []
for worker_id in range(NUM_WORKERS):
worker = Process(target=utils.run_worker, args=(worker_id, ))
workers.append(worker)
worker.start()
# -*- coding:utf-8 -*-
import glob
import pickle
import time
import zlib
from collections import deque
import numpy as np
from rl_main.main_constants import *
from rl_main.utils import exp_moving_average
import rl_main.rl_utils as rl_utils
env = rl_utils.get_environment(owner="worker")
class Worker:
def __init__(self, logger, worker_id, worker_mqtt_client):
self.worker_id = worker_id
self.worker_mqtt_client = worker_mqtt_client
self.rl_algorithm = rl_utils.get_rl_algorithm(env=env, worker_id=worker_id, logger=logger)
self.score = 0
self.global_max_ema_score = 0
self.global_min_ema_loss = 1000000000
self.local_scores = []
self.local_losses = []