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Reasoning and predicting POMDP planning complexity via covering numbers

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Abstract

Partially observable Markov decision processes (POMDPs) provide a rich mathematical framework for planning tasks in partially observable stochastic environments. The notion of the covering number, a metric of capturing the search space size of a POMDP planning problem, has been proposed as a complexity measure of approximate POMDP planning. Existing theoretical results are based on POMDPs with finite and discrete state spaces and measured in the l 1-metric space. When considering heuristics, they are assumed to be always admissible. This paper extends the theoretical results on the covering numbers of different search spaces, including the newly defined space reachable under inadmissible heuristics, to the l n-metric spaces. We provide a simple but scalable algorithm for estimating covering numbers. Experimentally, we provide estimated covering numbers of the search spaces reachable by following different policies on several benchmark problems, and analyze their abilities to predict the runtime of POMDP planning algorithms.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215000, China

    Zongzhang Zhang, Xiaofang Zhang & Quan Liu

  2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210000, China

    Zongzhang Zhang, Xiaofang Zhang & Quan Liu

  3. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215000, China

    Qiming Fu

  4. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Qiming Fu & Quan Liu

Authors
  1. Zongzhang Zhang
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  2. Qiming Fu
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  3. Xiaofang Zhang
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  4. Quan Liu
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Corresponding author

Correspondence to Quan Liu.

Additional information

Zongzhang Zhang received his PhD degree in computer science from University of Science and Technology of China, China in 2012. He is currently an associate professor at Soochow University, China. He worked as a research fellow at National University of Singapore, Singapore from 2012 to 2014 and as a visiting scholar at Rutgers University, USA from 2010 to 2011. His research directions include POMDPs, reinforcement learning, and multi-agent systems.

Qiming Fu received his PhD degree in computer science from Soochow University, China in 2014. He works as a lecturer at Soochow University of Science and Technology, China. His main research interests include reinforcement learning and Bayesian methods.

Xiaofang Zhang received her PhD degree in software engineering from Southeast University, China in 2008. She is now an associate professor in School of Computer Science and Technology at Soochow University, China. Her main research interests include software testing and reinforcement learning.

Quan Liu is now a professor and PhD supervisor in School of Computer Science and Technology at Soochow University, China. He received his PhD degree at Jilin University, China in 2004. He worked as a post-doctor at Nanjing University, China from 2006-2008. He is also a senior member of China Computer Federation. His main research interests include reinforcement learning, intelligence information processing, and automated reasoning.

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Zhang, Z., Fu, Q., Zhang, X. et al. Reasoning and predicting POMDP planning complexity via covering numbers. Front. Comput. Sci. 10, 726–740 (2016). https://doi.org/10.1007/s11704-015-5038-5

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  • DOI: https://doi.org/10.1007/s11704-015-5038-5

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