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Leveraging and Learning Propositional Functions for Large State Spaces in Planning and Reinforcement Learning

Planning in large state spaces is a normally computationally infeasible task that crops up in many practical robotics problems. I present a means of leveraging propositional functions in object-oriented Markov Decision Processes (OO-MDPs) to prune state spaces which yields both significant computation speedups as well as generally better plans when applied to baseline planners. Modern reinforcement learning (RL) solutions also make critical use of propositional functions. Given the importance of propositional functions in the presented pruning work and modern RL algorithms and the fact that propositional functions are often difficult to specify by hand, I also present a means by which useful propositional functions can be inferred based on observations regarding transition dynamics. I then use these propositional functions to derive a novel version of deterministic object-oriented RMAX (DOORMAX) which requires no expert-provided propositional functions. Lastly, not only do I demonstrate high correspondence between these learned propositional functions with expert-provided propositional functions but I also demonstrate near classic DOORMAX-level performance of my novel DOORMAX algorithm.