Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning

Titelangaben

Phan, Thomy ; Gabor, Thomas ; Müller, Robert ; Roch, Christoph ; Linnhoff-Popien, Claudia:
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning.
In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19). - s.l. : International Joint Conferences on Artificial Intelligence Organization , 2019 . - S. 5607-5613
ISBN 978-0-9992411-4-1
DOI: https://doi.org/10.24963/ijcai.2019/778

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Abstract

We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning. SYMBOL maintains an adaptive stack of Thompson Sampling bandits, whose size is bounded by the planning horizon and can be automatically adapted according to the underlying domain without any prior domain knowledge beyond a generative model. We empirically test SYMBOL in four large POMDP benchmark problems to demonstrate its effectiveness and robustness w.r.t. the choice of hyperparameters and evaluate its adaptive memory consumption. We also compare its performance with other open-loop planning algorithms and POMCP.