Parallel Algorithms for Solving Markov Decision Process

Authors:
Qi Zhang;Guangzhong Sun;Yinlong Xu
Affiliations:
Department of Computer Science, University of Science and Technology of China, Heifei, Anhui, P.R. China 230027;Department of Computer Science, University of Science and Technology of China, Heifei, Anhui, P.R. China 230027;Department of Computer Science, University of Science and Technology of China, Heifei, Anhui, P.R. China 230027
Venue:
ICA3PP '09 Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing
Year:
2009

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Abstract

Markov decision process (MDP) provides the foundations for a number of problems, such as artificial intelligence studying, automated planning and reinforcement learning. MDP can be solved efficiently in theory. However, for large scenarios, more investigations are needed to reveal practical algorithms. Algorithms for solving MDP have a natural concurrency. In this paper, we present parallel algorithms based on dynamic programming. Meanwhile, the cost of computation and communication complexity of this method is analyzed. Moreover, experimental results demonstrate excellent speedups and scalability.