def make_player(is_train=True, dump_dir=None): p = rl.GymRLEnviron(get_env('a3c.env_name'), dump_dir=dump_dir) p = rl.HistoryFrameProxyRLEnviron(p, get_env('a3c.nr_history_frames')) p = rl.LimitLengthProxyRLEnviron(p, get_env('a3c.max_nr_steps')) if is_train: p = rl.AutoRestartProxyRLEnviron(p) return p
def make_player(dump_dir=None): def resize_state(s): return image.resize(s, (84, 84), interpolation='NEAREST') p = rl.GymRLEnviron('Enduro-v0', dump_dir=dump_dir) p = rl.MapStateProxyRLEnviron(p, resize_state) p = rl.HistoryFrameProxyRLEnviron(p, 4) p = rl.LimitLengthProxyRLEnviron(p, 4000) return p
def make_player(is_train=True, dump_dir=None): def resize_state(s): return image.resize(s, get_env('a3c.input_shape'), interpolation='NEAREST') p = rl.GymRLEnviron(get_env('a3c.env_name'), dump_dir=dump_dir) p = rl.MapStateProxyRLEnviron(p, resize_state) p = rl.HistoryFrameProxyRLEnviron(p, get_env('a3c.nr_history_frames')) p = rl.LimitLengthProxyRLEnviron(p, get_env('a3c.max_nr_steps')) if is_train: p = rl.AutoRestartProxyRLEnviron(p) else: p = rl.GymPreventStuckProxyRLEnviron(p, get_env('a3c.inference.max_antistuck_repeat'), 1) return p
def make_player(dump_dir=None): p = rl.GymRLEnviron(get_env('ppo.env_name'), dump_dir=dump_dir) p = rl.LimitLengthProxyRLEnviron(p, get_env('ppo.max_nr_steps')) return p
def make_player(dump_dir=None): p = rl.GymRLEnviron('CartPole-v0', dump_dir=dump_dir) p = rl.LimitLengthProxyRLEnviron(p, 200) return p