  In recent years, a variety of tasks have been accomplished by deep
reinforcement learning (DRL). However, when applying DRL to tasks in a
real-world environment, designing an appropriate reward is difficult. Rewards
obtained via actual hardware sensors may include noise, misinterpretation, or
failed observations. The learning instability caused by these unstable signals
is a problem that remains to be solved in DRL. In this work, we propose an
approach that extends existing DRL models by adding a subtask to directly
estimate the variance contained in the reward signal. The model then takes the
feature map learned by the subtask in a critic network and sends it to the
actor network. This enables stable learning that is robust to the effects of
potential noise. The results of experiments in the Atari game domain with
unstable reward signals show that our method stabilizes training convergence.
We also discuss the extensibility of the model by visualizing feature maps.
This approach has the potential to make DRL more practical for use in noisy,
real-world scenarios.
