def copy(self, with_optimizer: bool = False):
model_copy = clone(self)
if not with_optimizer:
model_copy.optimizer = None
model_copy.scheduler = None
return model_copy
def dottable2dict(config):
if isinstance(config, float):
return str(config)
if not isinstance(config, dict):
return clone(config)
rt = {}
for k, v in config.items():
rt[k] = dottable2dict(v)
return rt
def __init__(self, model_dict: dict, optimizer_opt: Union[dict, tuple],
loss_func_dict: dict, hyper_params: DQNHyperParams):
if model_dict.get("target", None) is None:
model_dict["target"] = clone(model_dict["eval"])
super().__init__(model_dict, optimizer_opt, loss_func_dict,
hyper_params)
self._train_cnt = 0
self._device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
def __init__(self, model_dict: dict, optimizer_opt: Union[dict, tuple],
loss_func_dict: dict, hyper_params: DQNHyperParams):
"""
DQN algorithm. The model_dict must contain the key "eval". Optionally a model corresponding to
the key "target" can be provided. If the key "target" is absent or model_dict["target"] is None,
the target model will be a deep copy of the provided eval model.
"""
if model_dict.get("target", None) is None:
model_dict["target"] = clone(model_dict["eval"])
super().__init__(model_dict, optimizer_opt, loss_func_dict,
hyper_params)
self._train_cnt = 0
self._device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
def dumps(self):
"""Return a deep copy of store contents."""
return clone(dict(self._store))
def copy(self):
return clone(self)
