Information Gathering and Reward Exploitation of Subgoals for POMDPs

Authors

  • Hang Ma McGill University
  • Joelle Pineau McGill University

DOI:

https://doi.org/10.1609/aaai.v29i1.9659

Keywords:

POMDPs, planning under uncertainty, robot navigation

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 a weaker information-gathering capacity. In this paper, we propose 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" to 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 indicate that IGRES is a promising tool for POMDP planning in real-world settings.

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Published

2015-03-04

How to Cite

Ma, H., & Pineau, J. (2015). Information Gathering and Reward Exploitation of Subgoals for POMDPs. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9659