Fifty Years of RL in Games

author: Gerald Tesauro, IBM Thomas J. Watson Research Center
published: Aug. 26, 2009,   recorded: June 2009,   views: 6142

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Many researchers have advocated game domains as highly useful testbeds where one can cleanly isolate and study important issues faced by RL and more general AI methods in tackling messy real-world problems. In this talk, I'd like to survey some of the highlights of the numerous studies of RL in various game domains since Samuel's seminal work of fifty years ago. My definition of "games" is broad and will include puzzles, competitions, simulated marketplaces, and video/online games. I will also talk about the relationship and differences between traditional single-agent RL and more recent multiagent learning algorithms, which are likely necessary in general multi-player games. The goal of the talk is to draw a larger perspective on what we have learned from studying RL in games, and where promising future opportunities may lie, not only as RL theory advances, but as "games" themselves continue to evolve with advancing technology.

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Reviews and comments:

Comment1 Bradford, April 28, 2021 at 11:41 a.m.:

Thanks, the presentation was great.

Comment2 Arlene Padilla, April 29, 2021 at 6:42 a.m.:

My brother and I have been playing video games for a very long time because it's the best way to have a good time. I also want to tell you about the site which we periodically use to increase the rating in our favorite game. It seems to me that he, too, can be useful to you.

Comment3 endry shall, March 13, 2023 at 12:24 a.m.:

An interesting lecture, I saw some things as accepted in the company

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