FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large POMDPs
This work addresses the problem of efficient online planning in large POMDPs for the artificial intelligence community, representing an incremental advancement by building on existing heuristic and factored representation techniques.
The paper tackles the challenge of planning in large partially observable Markov decision processes (POMDPs) by introducing the FHHOP algorithm, which combines a hybrid heuristic search strategy with a factored state representation, resulting in substantial improvements in scalability and quality over state-of-the-art methods on benchmark problems.
Planning in partially observable Markov decision processes (POMDPs) remains a challenging topic in the artificial intelligence community, in spite of recent impressive progress in approximation techniques. Previous research has indicated that online planning approaches are promising in handling large-scale POMDP domains efficiently as they make decisions "on demand" instead of proactively for the entire state space. We present a Factored Hybrid Heuristic Online Planning (FHHOP) algorithm for large POMDPs. FHHOP gets its power by combining a novel hybrid heuristic search strategy with a recently developed factored state representation. On several benchmark problems, FHHOP substantially outperformed state-of-the-art online heuristic search approaches in terms of both scalability and quality.