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