def reset(self, reset_weight=True):
torch.cuda.empty_cache()
if reset_weight:
self.reset_network()
self.optimizer = optimizer_factory(self.config["optimizer"]["type"],
self._value_network.parameters(),
self.config["optimizer"]["learning_rate"],
self.config["optimizer"]["weight_decay"])
self.epoch = 0
def __init__(self, env, config=None):
super(DQNAgent, self).__init__(env, config)
self.value_net = model_factory(self.config["model"])
self.target_net = model_factory(self.config["model"])
self.target_net.load_state_dict(self.value_net.state_dict())
self.target_net.eval()
self.device = choose_device(self.config["device"])
self.value_net.to(self.device)
self.target_net.to(self.device)
self.loss_function = loss_function_factory(
self.config["loss_function"])
self.optimizer = optimizer_factory(self.config["optimizer"]["type"],
self.value_net.parameters(),
**self.config["optimizer"])
self.steps = 0
def __init__(self, env, config=None):
super(DQNAgent, self).__init__(env, config)
size_model_config(self.env, self.config["model"])
self.value_net = model_factory(self.config["model"])
self.target_net = model_factory(self.config["model"])
self.target_net.load_state_dict(self.value_net.state_dict())
self.target_net.eval()
logger.debug("Number of trainable parameters: {}".format(trainable_parameters(self.value_net)))
self.device = choose_device(self.config["device"])
self.value_net.to(self.device)
self.target_net.to(self.device)
self.loss_function = loss_function_factory(self.config["loss_function"])
self.optimizer = optimizer_factory(self.config["optimizer"]["type"],
self.value_net.parameters(),
**self.config["optimizer"])
self.steps = 0
def initialize_model(self):
self.value_net.reset()
self.optimizer = optimizer_factory(self.config["optimizer"]["type"],
self.value_net.parameters(),
**self.config["optimizer"])
