PUMA: Planning Under Uncertainty with Macro-Actions

Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan a different potential action for each future observation can be prohibitively expensive when planning many steps ahead. An efficient solution for planning far into the future in fully observable domains is to use temporallyextended sequences of actions, or "macro-actions." In this paper, we present a POMDP algorithm for planning under uncertainty with macro-actions (PUMA) that automatically constructs and evaluates open-loop macro-actions within forward-search planning, where the planner branches on observations only at the end of each macro-action. Additionally, we show how to incrementally refine the plan over time, resulting in an anytime algorithm that provably converges to an -optimal policy. In experiments on several large POMDP problems which require a long horizon lookahead, PUMA outperform...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Ruijie He, Emma Brunskill, Nicholas Roy
• Keywords: AAAI 2010, Intelligent Agents, Observable Domains, Partially Observable Markov Decision Process, Planning