print(s) env.close() if close: break return score if __name__ == "__main__": # init ES e = es.EvolutionStrategy( outw, 1.0, 50, # much smaller than distributed 15, min_sigma=1e-3, big_sigma=5e-2, wait_iter=100000) # multiprocessing pool = mp.Pool() LENGTH = 1000 times = 0 best = -float('inf') try: for i in range(1000): scores = [] pop = e.ask()
print(s) env.close() if close: break return score / n if __name__ == "__main__": # init ES e = es.EvolutionStrategy( outw, 1.0, 100, 10, min_sigma=1e-3, big_sigma=1e1, wait_iter=5 ) # multiprocessing pool = mp.Pool() LENGTH = 1000 times = 0 best = -float('inf') hist = open('car_es_hist.txt', 'w') try:
print(s) env.close() if close: break return score / 3 if __name__ == "__main__": # init ES e = es.EvolutionStrategy( outw, 1.0, 1000, 15, min_sigma=1e-3, big_sigma=5e-2, wait_iter=100000 ) # distributed training pool = distrib.DistributedServer() pool.start() print("Waiting for connections...") time.sleep(5) print("Done!") LENGTH = 1000 times = 0