  The agent learns to organize decision behavior to achieve a behavioral goal,
such as reward maximization, and reinforcement learning is often used for this
optimization. Learning an optimal behavioral strategy is difficult under the
uncertainty that events necessary for learning are only partially observable,
called as Partially Observable Markov Decision Process (POMDP). However, the
real-world environment also gives many events irrelevant to reward delivery and
an optimal behavioral strategy. The conventional methods in POMDP, which
attempt to infer transition rules among the entire observations, including
irrelevant states, are ineffective in such an environment. Supposing
Redundantly Observable Markov Decision Process (ROMDP), here we propose a
method for goal-oriented reinforcement learning to efficiently learn state
transition rules among reward-related "core states'' from redundant
observations. Starting with a small number of initial core states, our model
gradually adds new core states to the transition diagram until it achieves an
optimal behavioral strategy consistent with the Bellman equation. We
demonstrate that the resultant inference model outperforms the conventional
method for POMDP. We emphasize that our model only containing the core states
has high explainability. Furthermore, the proposed method suits online learning
as it suppresses memory consumption and improves learning speed.
