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dc.contributor.authorZhou, Jingzheen_US
dc.identifier.citationZhou, J. (2020). Combining PSR theory with distributional reinforcement learning. Master's thesis, Nanyang Technological University, Singapore.en_US
dc.description.abstractThis work focuses on using Distributional Reinforcement Learning (DRL) in a partially observable environment that is modelled via Predictive State Representation Theory (PSR). We aim to integrate the benefits of DRL and PSR to obtain a model-based reinforcement learning method that is capable of providing complete (distributional) performance information about a policy using an observation-only environment model. PSR theory is one of the advanced techniques used to model a dynamical system on a partially observable environment. Unlike traditional partially observable Markov models, such as POMDP, which capture the uncertainty of the environment using belief states, PSR model describes the partially observable environment based on probabilities of executable and observable future events. Distributional Reinforcement Learning (DRL), proposed by MG Bellemare, is a learning paradigm that aims to improve learning by modelling the rewards as probability distributions instead of scalar expectations.en_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleCombining PSR theory with distributional reinforcement learningen_US
dc.typeThesis-Master by Researchen_US
dc.contributor.supervisorZinovi Rabinovichen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
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