Thesis Summary: Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains

摘要

The objective of my doctoral research is bring together two fields: partially-observable reinforcement learning (PORL) and non-parametric Bayesian statistics (NPB) to address issues of statistical modeling and decision-making in complex, realworld domains.