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Bayesian optimistic Kullback-Leibler exploration
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DOI：10.1007/s10994-018-5767-4.png)
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Abstract
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We consider a Bayesian approach to model-based reinforcement learning, where the agent uses a distribution of environment models to find the action that optimally trades off exploration and exploitation. Unfortunately, it is intractable to find the Bayes-optimal solution to the problem except for restricted cases. In this paper, we present BOKLE, a simple algorithm that uses Kullback-Leibler divergence to constrain the set of plausible models for guiding the exploration. We provide a formal analysis that this algorithm is near Bayes-optimal with high probability. We also show an asymptotic relation between the solution pursued by BOKLE and a well-known algorithm called Bayesian exploration bonus. Finally, we show experimental results that clearly demonstrate the exploration efficiency of the algorithm.
Keywords:
Model-based Bayesian reinforcement learning
Bayes-adaptive Markov decision process
PAC-BAMDP
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