Constrained Hierarchical Monte Carlo Belief-State Planning
Arec Jamgochian,Hugo Buurmeijer,Kyle H. Wray,Anthony Corso,Mykel J. Kochenderfer,Arec Jamgochian,Hugo Buurmeijer,Kyle H. Wray,Anthony Corso,Mykel J. Kochenderfer
Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning under state and transition uncertainty. Unfortunately, online CPOMDP planning is extremely difficult in large or continuous problem domains. In many large robotic domains, hierarchical decomposition can simplify planning...
