论文信息 - Approximate Planning for Factored POMDPs using Belief State Simplification

Approximate Planning for Factored POMDPs using Belief State Simplification

We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for factored POMDPs that exploits the accuracy efficiency tradeoff in the belief state simplification introduced by Boyen and Koller.

David A. McAllester | Satinder P. Singh | Satinder Singh

[1] Edward J. Sondik,et al. The Optimal Control of Partially Observable Markov Processes over the Infinite Horizon: Discounted Costs , 1978, Oper. Res..

[2] John N. Tsitsiklis,et al. The Complexity of Markov Decision Processes , 1987, Math. Oper. Res..

[3] John N. Tsitsiklis,et al. Parallel and distributed computation , 1989 .

[4] Thomas M. Cover,et al. Elements of Information Theory , 2005 .

[5] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .

[6] Michael L. Littman,et al. Algorithms for Sequential Decision Making , 1996 .

[7] Xavier Boyen,et al. Tractable Inference for Complex Stochastic Processes , 1998, UAI.

[8] Richard S. Sutton,et al. Introduction to Reinforcement Learning , 1998 .