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2014/02/13, MC103, 12:10 - 12:40

Information Gathering and Reward Exploitation of Subgoals for POMDPs
Hang Ma , MSc Candidate, SOCS

Abstract:

Planning in large partially observable Markov decision processes (POMDPs) is challenging especially when a long planning horizon is required. A few recent algorithms successfully tackle this case but at the expense of weaker information-gathering capacity. In this paper, we propose \emph{Information Gathering and Reward Exploitation of Subgoals} (IGRES), a randomized POMDP planning algorithm that leverages information in the state space to automatically generate ``macro-actions'' that can tackle tasks with long planning horizons, while locally exploring the belief space to allow effective information gathering. Experimental results show that IGRES is an effective multi-purpose POMDP solver, providing state-of-the-art performance for both long horizon planning tasks and information gathering tasks on benchmark domains. Additional experiments with an ecological adaptive management problem presented in the IJCAI 2013 data challenge track indicate that IGRES is a promising tool for POMDP planning in real-world settings.