Python baselines.common.tf_util.Uint8Input() Examples

The following are 6 code examples of baselines.common.tf_util.Uint8Input(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module baselines.common.tf_util , or try the search function .
Example #1
Source File: wang2015_eval.py    From distributional-dqn with MIT License 6 votes vote down vote up 
def main():
    set_global_seeds(1)
    args = parse_args()

    with U.make_session(4) as sess:  # noqa
        _, env = make_env(args.env)
        model_parent_path = distdeepq.parent_path(args.model_dir)
        old_args = json.load(open(model_parent_path + '/args.json'))

        act = distdeepq.build_act(
            make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
            p_dist_func=distdeepq.models.atari_model(),
            num_actions=env.action_space.n,
            dist_params={'Vmin': old_args['vmin'],
                         'Vmax': old_args['vmax'],
                         'nb_atoms': old_args['nb_atoms']})
        U.load_state(os.path.join(args.model_dir, "saved"))
        wang2015_eval(args.env, act, stochastic=args.stochastic)
Example #2
Source File: wang2015_eval.py    From learning2run with MIT License 5 votes vote down vote up 
def main():
    set_global_seeds(1)
    args = parse_args()
    with U.make_session(4) as sess:  # noqa
        _, env = make_env(args.env)
        act = deepq.build_act(
            make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
            q_func=dueling_model if args.dueling else model,
            num_actions=env.action_space.n)

        U.load_state(os.path.join(args.model_dir, "saved"))
        wang2015_eval(args.env, act, stochastic=args.stochastic)
Example #3
Source File: wang2015_eval.py    From rl-attack-detection with MIT License 5 votes vote down vote up 
def main():
    set_global_seeds(1)
    args = parse_args()
    with U.make_session(4) as sess:  # noqa
        _, env = make_env(args.env)
        act = deepq.build_act(
            make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
            q_func=dueling_model if args.dueling else model,
            num_actions=env.action_space.n)

        U.load_state(os.path.join(args.model_dir, "saved"))
        wang2015_eval(args.env, act, stochastic=args.stochastic)
Example #4
Source File: wang2015_eval.py    From deeprl-baselines with MIT License 5 votes vote down vote up 
def main():
    set_global_seeds(1)
    args = parse_args()
    with U.make_session(4):  # noqa
        _, env = make_env(args.env)
        act = deepq.build_act(
            make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
            q_func=dueling_model if args.dueling else model,
            num_actions=env.action_space.n)

        U.load_state(os.path.join(args.model_dir, "saved"))
        wang2015_eval(args.env, act, stochastic=args.stochastic)
Example #5
Source File: wang2015_eval.py    From emdqn with MIT License 5 votes vote down vote up 
def main():
    set_global_seeds(1)
    args = parse_args()
    with U.make_session(4) as sess:  # noqa
        _, env = make_env(args.env)
        act = deepq.build_act(
            make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
            q_func=dueling_model if args.dueling else model,
            num_actions=env.action_space.n)

        U.load_state(os.path.join(args.model_dir, "saved"))
        wang2015_eval(args.env, act, stochastic=args.stochastic)
Example #6
Source File: wang2015_eval.py    From BackpropThroughTheVoidRL with MIT License 5 votes vote down vote up 
def main():
    set_global_seeds(1)
    args = parse_args()
    with U.make_session(4):  # noqa
        _, env = make_env(args.env)
        act = deepq.build_act(
            make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
            q_func=dueling_model if args.dueling else model,
            num_actions=env.action_space.n)

        U.load_state(os.path.join(args.model_dir, "saved"))
        wang2015_eval(args.env, act, stochastic=args.stochastic)