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General Reinforcement Learning

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Reinforcement learning problems are often phrased in terms of Markov decision processes (MDPs). In this talk, we go beyond MDPs and consider reinforcement learning in environments that are non-Markovian, non-ergodic and only partially observable. Our focus will not be on practical algorithms, but rather on the fundamental underlying problems. How do we balance exploration and exploitation? How do we explore optimally? When is an agent optimal? We introduce the Bayesian agent AIXI , point out some of its problems, and discuss potential solutions.

Speaker: Jan Leike is a PhD student at the Australian National University working with Marcus Hutter.

This talk is part of the Machine Learning @ CUED series.

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