Thesis Summary: Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains
نویسنده
چکیده
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 decisionmaking in complex, realworld domains.
منابع مشابه
Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains
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 decisionmaking in complex, realworld domains.
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Making intelligent decisions from incomplete information is critical in many applications: for example, medical decisions must often be made based on a few vital signs, without full knowledge of a patient’s condition, and speech-based interfaces must infer a user’s needs from noisy microphone inputs. What makes these tasks hard is that we do not even have a natural representation with which to ...
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