Beating the world's best at Super Smash Bros. with deep reinforcement learning
Name
986497700-MIT.pdf
Description
Full printable version
Size
1.45 MB
Format
Adobe PDF
Checksum (MD5)
a9579cf1b5bc03e3ddf13ad2fdf3287d
Author(s)
Firoiu, Vlad
Advisor(s)
Joshua B. Tenenbaum.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
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..
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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