Better Optimism By Bayes: Adaptive Planning with Rich Models

Arthur Guez; David Silver; Peter Dayan

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arXiv · 1402.1958

Better Optimism By Bayes: Adaptive Planning with Rich Models

Arthur Guez·David Silver·Peter Dayan

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Abstract

The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We ask whether it is feasible and truly beneficial to combine rich probabilistic models with a closer approximation to fully Bayesian planning. First, we use a collection of counterexamples to show formal problems with the over-optimism inherent in Thompson sampling. Then we leverage state-of-the-art techniques in efficient Bayes-adaptive planning and non-parametric Bayesian methods to perform qualitatively better than both existing conventional algorithms and Thompson sampling on two contextual bandit-like problems.

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Arthur Guez, David Silver, Peter Dayan. 2014-02-09. Better Optimism By Bayes: Adaptive Planning with Rich Models. https://arxiv.org/abs/1402.1958

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