def __init__(self, value_network, config, writer=None):
self.config = config
# Load configs
self.betas_for_duplication = parse(self.config["betas_for_duplication"])
self.betas_for_discretisation = parse(self.config["betas_for_discretisation"])
self.loss_function = loss_function_factory(self.config["loss_function"])
self.loss_function_c = loss_function_factory(self.config["loss_function_c"])
self.device = choose_device(self.config["device"])
# Load network
self._value_network = value_network
self._value_network = self._value_network.to(self.device)
self.n_actions = self._value_network.predict.out_features // 2
self.writer = writer
if writer:
self.writer.add_graph(self._value_network,
input_to_model=torch.tensor(np.zeros((1, 1, self._value_network.size_state + 1),
dtype=np.float32)).to(self.device))
self.memory = ReplayMemory(transition_type=TransitionBFTQ, config=self.config)
self.optimizer = None
self.batch = 0
self.epoch = 0
self.reset()
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