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Konferenzbeitrag

Reinforcement Learning and the Bayesian Control Rule

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84121

Ortega,  PA
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83827

Braun,  DA
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BADC-6
Zusammenfassung
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.