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The agent learns to organize decision behavior to achieve a behavioral goal,such as reward maximization, and reinforcement learning is often used for thisoptimization. Learning an optimal behavioral strategy is difficult under theuncertainty that events necessary for learning are only partially observable,called as Partially Observable Markov Decision Process (POMDP). However, thereal-world environment also gives many events irrelevant to reward delivery andan optimal behavioral strategy. The conventional methods in POMDP, whichattempt to infer transition rules among the entire observations, includingirrelevant states, are ineffective in such an environment. SupposingRedundantly Observable Markov Decision Process (ROMDP), here we propose amethod for goal-oriented reinforcement learning to efficiently learn statetransition rules among reward-related "core states'' from redundantobservations. Starting with a small number of initial core states, our modelgradually adds new core states to the transition diagram until it achieves anoptimal behavioral strategy consistent with the Bellman equation. Wedemonstrate that the resultant inference model outperforms the conventionalmethod for POMDP. We emphasize that our model only containing the core stateshas high explainability. Furthermore, the proposed method suits online learningas it suppresses memory consumption and improves learning speed.

Goal-oriented inference of environment from redundant observations