84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
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import argparse
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import config
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def print_train_outcome(outcome, init_ep = 0):
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format_vars = { 'init_ep': init_ep,
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'count': len(train_outcome),
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'sum': sum(train_outcome),
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'mean': sum(train_outcome) / len(train_outcome)}
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print("train;{init_ep};{count};{sum};{mean}".format(**format_vars))
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def print_eval_outcomes(outcomes, init_ep = 0):
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for outcome in eval_outcomes:
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scores = outcome[1]
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format_vars = { 'init_ep': init_ep,
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'method': outcome[0],
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'count': len(scores),
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'sum': sum(scores),
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'mean': sum(scores) / len(scores)
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}
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print("eval;{method};{init_ep};{count};{sum};{mean}".format(**format_vars))
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parser = argparse.ArgumentParser(description="Backgammon games")
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parser.add_argument('--episodes', action='store', dest='episode_count',
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type=int, default=1000,
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help='number of episodes to train')
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parser.add_argument('--model-path', action='store', dest='model_path',
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default='./model',
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help='path to Tensorflow model')
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parser.add_argument('--eval-methods', action='store',
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default=['random'], nargs='*',
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help='specifies evaluation methods')
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parser.add_argument('--eval', action='store_true',
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help='whether to evaluate the neural network with a random choice bot')
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parser.add_argument('--train', action='store_true',
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help='whether to train the neural network')
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parser.add_argument('--play', action='store_true',
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help='whether to play with the neural network')
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args = parser.parse_args()
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config = {
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'model_path': args.model_path,
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'episode_count': args.episode_count,
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'eval_methods': args.eval_methods,
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'train': args.train,
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'play': args.play,
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'eval': args.eval
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}
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#print("-"*30)
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#print(type(args.eval_methods))
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#print(args.eval_methods)
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#print("-"*30)
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import game
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g = game.Game(config = config)
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g.set_up_bots()
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episode_count = args.episode_count
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if args.train:
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eps = 0
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while True:
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train_outcome = g.train_model(episodes = episode_count, init_ep = eps)
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print_train_outcome(train_outcome, init_ep = eps)
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if args.eval:
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eval_outcomes = g.eval(init_ep = eps)
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print_eval_outcomes(eval_outcomes, init_ep = eps)
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eps += episode_count
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elif args.eval:
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outcomes = g.eval()
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print_eval_outcomes(outcomes, init_ep = 0)
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#elif args.play:
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# g.play(episodes = episode_count)
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#outcomes = g.play(2000)
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#print(outcomes)
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#print(sum(outcomes))
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#count = g.play()
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# highest = max(highest,count)
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# except KeyboardInterrupt:
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# break
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#print("\nHighest amount of turns is:",highest)
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