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Reinforcement Learning (RL) is limited in practice by its gray-box nature,which is responsible for insufficient trustiness from users, unsatisfiedinterpretation for human intervention, inadequate analysis for futureimprovement, etc. This paper seeks to partially characterize the interplaybetween dynamical environments and the DOB-net. The DOB-net obtained from RLsolves a set of Partially Observable Markovian Decision Processes (POMDPs). Thetransition function of each POMDP is largely determined by the environments,which are excessive external disturbances in this research. This paper proposesan Attention-based Abstraction (A${}^2$) approach to extract a finite-stateautomaton, referred to as a Key Moore Machine Network (KMMN), to capture theswitching mechanisms exhibited by the DOB-net in dealing with multiple suchPOMDPs. This approach first quantizes the controlled platform by learningcontinuous-discrete interfaces. Then it extracts the KMMN by finding the keyhidden states and transitions that attract sufficient attention from theDOB-net. Within the resultant KMMN, this study found three patterns of cyclicswitchings (between key hidden states), showing controls near their saturationare synchronized with unknown disturbances. Interestingly, the found switchingmechanism has appeared previously in the design of hybrid control foroften-saturated systems. It is further interpreted via an analogy to thediscrete-event subsystem in the hybrid control.

A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances