def METHOD_NAME(self):
    action = self.agent.select_action(self.observation, noise_factor=self.args.noise_factor)
    self.observation, reward, done, _ = self.env.step(action)
    self.agent.observe(reward, self.observation, done, self.step)
    state, action, reward, \
        next_state, terminal = self.agent.memory.sample_batch(self.batch_size, self.device)
    state = torch.cat((state[:, :6].float() / 255, state[:, 6:7].float() / self.args.max_step,
                       self.agent.coord.expand(state.shape[0], 2, 128, 128)), 1)
    return self.agent.actor, (state, )