コード例 #1
0
def main(unused_argv):
  config = Config(FLAGS.sz, FLAGS.map, -1) # 进行参数的设置
  os.makedirs('weights/' + config.full_id(), exist_ok=True)
  cfg_path = 'weights/%s/config.json' % config.full_id() # 保存参数的位置
  config.build(cfg_path) # 建立和设置参数

  if FLAGS.human:
    human()
  else:
    agent(config)
コード例 #2
0
    parser.add_argument("--map", type=str, default='MoveToBeacon')
    parser.add_argument("--cfg_path", type=str, default='config.json.dist')
    parser.add_argument("--restrict", type=bool, default=False)
    parser.add_argument("--imitation", type=bool, default=False)
    parser.add_argument("--test", type=bool, nargs='?', const=True, default=False)
    parser.add_argument("--restore", type=bool, nargs='?', const=True, default=False)
    parser.add_argument('--save_replay', type=bool, nargs='?', const=True, default=False)
    args = parser.parse_args()

    os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) #environ是一个字符串所对应环境的映像对象,输入为要使用的GPU number
    tf.reset_default_graph()
    sess = tf.Session()

    # config = Config(args.sz, args.map, lambda _: 1)
    config = Config(args.sz, args.map, args.run_id, restrict=args.restrict, imitation=args.imitation) # 进行参数的设置
    os.makedirs('weights/' + config.full_id(), exist_ok=True)
    cfg_path = 'weights/%s/config.json' % config.full_id() # 保存参数的位置
    config.build(cfg_path if args.restore else args.cfg_path) # 建立和设置参数
    if not args.restore and not args.test:
        config.save(cfg_path) # 保存参数

    envs = EnvWrapper(make_envs(args), config) # 创建环境,封装一层
    agent = A2CAgent(sess, fully_conv, config, args.restore, args.discount, args.lr, args.vf_coef, args.ent_coef, args.clip_grads) # 创建agent

    runner = Runner(envs, agent, args.steps) # 创建进程
    runner.run(args.updates, not args.test) # 开始运行

    if args.save_replay: #是否保存回放
        envs.save_replay()

    envs.close() # 关闭环境
コード例 #3
0
ファイル: main.py プロジェクト: MCCCSunny/PMES
    # preprocess args
    if args.maps == '':
        args.maps = '{"' + args.map + '": ' + str(args.envs) + '}'

    args.maps = json.loads(args.maps, object_pairs_hook=OrderedDict)

    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
    os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)

    # Allow gpu growth
    tf_config = tf.ConfigProto()
    tf_config.gpu_options.allow_growth = True

    config = Config(args.sz, '_'.join(args.maps), args.run_id)
    weight_dir = os.path.join(args.work_dir, 'weights', config.full_id())
    log_dir = os.path.join(args.work_dir, 'logs', config.full_id())
    print('weights are saved at ', weight_dir)
    os.makedirs(weight_dir, exist_ok=True)
    cfg_path = os.path.join(weight_dir, 'config.json')

    if args.restore and (not os.path.isfile(os.path.join(weight_dir, 'checkpoint'))) and args.remote_restore != '':
        args.restore_path = args.remote_restore
        config.build(os.path.join(args.restore_path, 'config.json'))
        config.save(cfg_path)
        assert os.path.isfile(os.path.join(args.restore_path, 'checkpoint'))
    elif args.restore:
        if os.path.isfile(cfg_path):
            config.build(cfg_path)
        else:
            config.build(args.cfg_path)
コード例 #4
0
    args.action_dim = opt_dim

    # max_step from float --> int
    if len(args.map) > 5 and (args.map.startswith('Build')):
        args.max_step = int(args.num_timesteps / args.step_mul)
    else:
        args.max_step = int(args.num_timesteps / args.step_mul)
    args.game_steps_per_episode = args.max_step * args.step_mul

    # pytorch device setup
    processor = 'cpu' if args.eval or args.gpu == -1 else 'cuda:%s'%args.gpu
    device = torch.device(processor)
    data_format = "NHWC" if args.nhwc else "NCHW"

    config = Config(args, device, data_format)
    os.makedirs('weights/' + config.full_id(), exist_ok=True)
    with open('weights/' + config.full_id() + '/arguments.txt', 'w') as f:
        for key,value in vars(args).items():
            string = str(key) + ' : ' + str(value) + '\n'
            f.write( string )

    # make/clean directory for save mode
    dirname = os.path.join('results', args.map, args.meta, 'run_%d'%(args.run_id))
    if args.save_ilp or (args.meta in ['random', 'hrl'] and not args.eval):
        if os.path.exists(dirname):
            print("The savepath already exists. Deleting {}".format(dirname))
            shutil.rmtree(dirname)
        os.makedirs(dirname, exist_ok=True)

    # set up agent & ilp
    ilp = None