momentum = args.momentum # Create the game specs. game_specs = GameResultSpecs(info_interval_current, info_interval_mean, agent_save_interval, results_save_interval, plots_name_prefix, results_name_prefix, agent_name_prefix, recording_name_prefix, plot_train_results, save_plots) # Create the game. game = Game(episodes, downsample_scale, agent_frame_history, steps_per_action, fit_frequency, no_operation, game_specs, render, record) # Create the optimizer. optimizer = initialize_optimizer(optimizer_name, learning_rate, beta1, beta2, lr_decay, rho, fuzz, momentum) # Create the policy. policy = EGreedyPolicy(epsilon, final_epsilon, epsilon_decay, total_observe_count, game.action_space_size) # Create the agent. agent = create_agent() # Check arguments. run_checks() # Play the game, using the agent. game.play_game(agent)
from game_engine.game import Game from game_engine.player import RandomPlayer import time import sys sys.path.append('../') games = 5000 players = [RandomPlayer() for _ in range(4)] initial = time.perf_counter() last = initial for i in range(games): if i % 100 == 0: print("{}/{} time: {}".format(i, games, time.perf_counter() - last)) last = time.perf_counter() wiz = Game(players=players) wiz.play_game() print(time.perf_counter() - initial)