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dc.contributor.advisorKuzovkin, Ilya, juhendaja
dc.contributor.advisorMatiisen, Tambet, juhendaja
dc.contributor.authorRing, Roman
dc.contributor.otherTartu Ülikool. Matemaatika ja statistika instituutet
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.date.accessioned2018-06-28T08:12:16Z
dc.date.available2018-06-28T08:12:16Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10062/61039
dc.description.abstractReinforcement Learning (RL) is a subfield of Artificial Intelligence (AI) that deals with agents navigating in an environment with the goal of maximizing total reward. Games are good environments to test RL algorithms as they have simple rules and clear reward signals. Theoretical part of this thesis explores some of the popular classical and modern RL approaches, which include the use of Artificial Neural Network (ANN) as a function approximator inside AI agent. In practical part of the thesis we implement Advantage Actor-Critic RL algorithm and replicate ANN based agent described in [Vinyals et al., 2017]. We reproduce the state-of-the-art results in a modern video game StarCraft II, a game that is considered the next milestone in AI after the fall of chess and Go.et
dc.language.isoenget
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Estonia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/ee/*
dc.subjectreinforcement learningen
dc.subjectartificial neural networksen
dc.subjectstiimulõpeet
dc.subjecttehisnärvivõrgudet
dc.titleReplicating DeepMind StarCraft II reinforcement learning benchmark with actor-critic methodsen
dc.typeinfo:eu-repo/semantics/bachelorThesisen


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