2026-09-19T13:14:28Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1089842022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Joshua B. Tenenbaum.
Firoiu, Vlad
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2017-05-11T19:59:09Z
2017-05-11T19:59:09Z
2017
2017
http://hdl.handle.net/1721.1/108984
986497700
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 29).
There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now solvable by fairly generic algorithms, based on deep learning, that learn to play from experience with often minimal knowledge of the specific domain of interest. In this work, we will investigate the performance of these methods on Super Smash Bros. Melee (SSBM), a popular multiplayer fighting game. The SSBM environment has complex dynamics and partial observability, making it challenging for man and machine alike. The multiplayer aspect poses an additional challenge, as the vast majority of recent advances in RL have focused on single-agent environments. Nonetheless, we will show that it is possible to train agents that are competitive against and even surpass human professionals, a new result for the video game setting..
by Vlad Firoiu.
S.M.
29 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Beating the world's best at Super Smash Bros. with deep reinforcement learning
Thesis
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<Abstract>There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now solvable by fairly generic algorithms, based on deep learning, that learn to play from experience with often minimal knowledge of the specific domain of interest. In this work, we will investigate the performance of these methods on Super Smash Bros. Melee (SSBM), a popular multiplayer fighting game. The SSBM environment has complex dynamics and partial observability, making it challenging for man and machine alike. The multiplayer aspect poses an additional challenge, as the vast majority of recent advances in RL have focused on single-agent environments. Nonetheless, we will show that it is possible to train agents that are competitive against and even surpass human professionals, a new result for the video game setting..</Abstract>
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