def __init__(self): self.context_params = ContextParams() self.args = ArgsProvider(define_params=[ ("handicap_level", 0), ("latest_start", 1000), ("latest_start_decay", 0.7), ("fs_ai", 50), ("fs_opponent", 50), ("ai_type", dict(type=str, choices=[ "AI_SIMPLE", "AI_HIT_AND_RUN", "AI_NN", "AI_FLAG_NN", "AI_TD_NN" ], default="AI_NN")), ("opponent_type", dict(type=str, choices=[ "AI_SIMPLE", "AI_HIT_AND_RUN", "AI_FLAG_SIMPLE", "AI_TD_BUILT_IN" ], default="AI_SIMPLE")), ("max_tick", dict(type=int, default=30000, help="Maximal tick")), ("mcts_threads", 64), ("seed", 0), ("simple_ratio", -1), ("ratio_change", 0), ("actor_only", dict(action="store_true")) ], more_params=["batchsize", "T"], child_providers=[self.context_params.args])
def __init__(self): self.args = ArgsProvider(call_from=self, define_params=[ ("num_minibatch", 5000), ("num_episode", 10000), ("tqdm", dict(action="store_true")), ])
def __init__(self): self.args = ArgsProvider( call_from = self, define_params = [ ("stats", dict(type=str, choices=["rewards", "winrate"], default="rewards")), ("num_eval", 500) ] )
def __init__(self): super(EvaluationProcess, self).__init__() self.server = ParameterServer(2) self.args = ArgsProvider(call_from=self, define_params=[ ("eval_freq", 10), ("eval_gpu", 1), ]) self.count = 0
def __init__(self): self.args = ArgsProvider( call_from=self, define_args=[("num_games", 1024), ("batchsize", 128), ("game_multi", dict(type=int, default=None)), ("T", 6), ("eval", dict(action="store_true")), ("wait_per_group", dict(action="store_true")), ("verbose_comm", dict(action="store_true")), ("verbose_collector", dict(action="store_true"))], on_get_args=self._on_get_args)
def __init__(self, model_class): self.model_class = model_class self.args = ArgsProvider(call_from=self, define_params=[("gpu", dict(type=str, help="gpu to use", default=None)), ("load", dict(type=str, help="load model", default=None))])
def __init__(self): self.context_args = ContextArgs() self.args = ArgsProvider(call_from=self, define_args=[ ("frame_skip", 4), ("hist_len", 4), ("rom_file", "pong.bin"), ("actor_only", dict(action="store_true")), ("reward_clip", 1), ("rom_dir", os.path.dirname(__file__)) ], more_args=["batchsize", "T"], child_providers=[self.context_args.args])
def __init__(self): self.timer = RLTimer() self.last_time = None self.args = ArgsProvider( call_from=self, define_params=[ ("freq_update", 1), ("record_dir", "./record"), ("save_prefix", "save"), ("save_dir", dict(type=str, default=None)), ], more_params=["num_games", "batchsize", "num_minibatch"], on_get_params=self._on_get_params)
def __init__(self, mi=None, args=None): if args is None: self.args = ArgsProvider(call_from=self, define_params=self._params(), on_get_params=self._init) else: self.args = args self._init(args) # Accumulated errors. self.stats = defaultdict(lambda: Stats()) self._cb = {} if mi is not None: self.model_interface = mi
def __init__(self): self.args = ArgsProvider( call_from=self, define_params=[ ("sample_policy", dict(type=str, choices=["epsilon-greedy", "multinomial", "uniform"], help="Sample policy", default="epsilon-greedy")), ("sample_node", dict(type=str, default="pi")), ("greedy", dict(action="store_true")), ("epsilon", dict(type=float, help="Used in epsilon-greedy approach", default=0.00)) ])