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A crash-course on Bayesian Reinforcement Learning

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If you have a question about this talk, please contact Yingzhen Li.

In this talk we will follow the recent survey on Bayesian Reinforcement Learning from Ghavamzadeh et al (2015) to introduce the key concepts and recent developments of Bayesian Reinforcement Learning. The talk assumes no previous knowledge of reinforcement learning and will cover fundamentals, model-based reinforcement learning, model-free reinforcement learning. Should there be enough time, pointers for advanced topics will be also discussed.

There is no need to read any material prior to the meeting.

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

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