Action Selection in Bayesian Reinforcement Learning

Tao Wang

My research attempts to address on-line action selection in reinforcement learning from a Bayesian perspective. The idea is to develop more effective action selection techniques by exploiting information in a Bayesian posterior, while also selecting actions by growing an adaptive, sparse lookahead tree. I further augment the approach by considering a new value function approximation strategy for the belief-state Markov decision processes induced by Bayesian learning.

Subjects: 12. Machine Learning and Discovery; 12.1 Reinforcement Learning


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