Reinforcement Learning and the Bayesian Control Rule

Ortega, PA; Braun, DA

doi:10.1007/978-3-642-22887-2_30

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Reinforcement Learning and the Bayesian Control Rule

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https://link.springer.com/content/pdf/10.1007%2F978-3-642-22887-2_30.pdf
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Citation

Ortega, P., & Braun, D. (2011). Reinforcement Learning and the Bayesian Control Rule. In S. Godsill, J. Schmidhuber, K. Thórisson, & M. Looks (Eds.), Artificial General Intelligence (pp. 281-285). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BADC-6

Abstract

We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intractable planning problem reduces to a simple multi-armed bandit problem, where each lever stands for a potentially arbitrarily complex policy. Furthermore, we use the Bayesian control rule to construct an adaptive bandit player that is universal with respect to a given class of optimal bandit players, thus indirectly constructing an adaptive agent that is universal with respect to a given class of policies.