def set_writer(self, writer):
super().set_writer(writer)
obs_shape = self.env.observation_space.shape if isinstance(self.env.observation_space, spaces.Box) else \
self.env.observation_space.spaces[0].shape
model_input = torch.zeros((1, *obs_shape), dtype=torch.float, device=self.device)
self.writer.add_graph(self.value_net, input_to_model=(model_input,)),
self.writer.add_scalar("agent/trainable_parameters", trainable_parameters(self.value_net), 0)
def set_writer(self, writer):
super().set_writer(writer)
self.writer.add_graph(self.value_net,
input_to_model=torch.zeros(
(1, *self.env.observation_space.shape),
dtype=torch.float,
device=self.device))
self.writer.add_scalar("agent/trainable_parameters",
trainable_parameters(self.value_net), 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
