  We tackle the blackbox issue of deep neural networks in the settings of
reinforcement learning (RL) where neural agents learn towards maximizing reward
gains in an uncontrollable way. Such learning approach is risky when the
interacting environment includes an expanse of state space because it is then
almost impossible to foresee all unwanted outcomes and penalize them with
negative rewards beforehand. Unlike reverse analysis of learned neural features
from previous works, our proposed method \nj{tackles the blackbox issue by
encouraging} an RL policy network to learn interpretable latent features
through an implementation of a disentangled representation learning method.
Toward this end, our method allows an RL agent to understand self-efficacy by
distinguishing its influences from uncontrollable environmental factors, which
closely resembles the way humans understand their scenes. Our experimental
results show that the learned latent factors not only are interpretable, but
also enable modeling the distribution of entire visited state space with a
specific action condition. We have experimented that this characteristic of the
proposed structure can lead to ex post facto governance for desired behaviors
of RL agents.
