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A state space representation of an environment is a classic and yet powerfultool used by many autonomous robotic systems for efficient and often optimalsolution planning. However, designing these representations with highperformance is laborious and costly, necessitating an effective and versatiletool for autonomous generation of state spaces for autonomous robots. Wepresent a novel state space exploration algorithm by extending the originalsurprise-based partially-observable Markov Decision Processes (sPOMDP), anddemonstrate its effective long-term exploration planning performance in variousenvironments. Through extensive simulation experiments, we show the proposedmodel significantly increases efficiency and scalability of the original sPOMDPlearning techniques with a range of 31-63% gain in training speed whileimproving robustness in environments with less deterministic transitions. Ourresults pave the way for extending sPOMDP solutions to a broader set ofenvironments.

Autonomous sPOMDP Environment Modeling With Partial Model Exploitation