Replicating DeepMind StarCraft II reinforcement learning benchmark with actor-critic methods
| dc.contributor.advisor | Kuzovkin, Ilya, juhendaja | |
| dc.contributor.advisor | Matiisen, Tambet, juhendaja | |
| dc.contributor.author | Ring, Roman | |
| dc.contributor.other | Tartu Ülikool. Matemaatika ja statistika instituut | et |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.date.accessioned | 2018-06-28T08:12:16Z | |
| dc.date.available | 2018-06-28T08:12:16Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Reinforcement 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.identifier.uri | http://hdl.handle.net/10062/61039 | |
| dc.language.iso | eng | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Estonia | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ee/ | * |
| dc.subject | reinforcement learning | en |
| dc.subject | artificial neural networks | en |
| dc.subject | stiimulõpe | et |
| dc.subject | tehisnärvivõrgud | et |
| dc.title | Replicating DeepMind StarCraft II reinforcement learning benchmark with actor-critic methods | en |
| dc.type | info:eu-repo/semantics/bachelorThesis | en |