Combining M-MCTS and Deep Reinforcement Learning for General Game Playing

2022 ◽  
pp. 221-234
Author(s):  
Sili Liang ◽  
Guifei Jiang ◽  
Yuzhi Zhang
Keyword(s):  
Reinforcement Learning ◽  
Game Playing ◽  
General Game Playing ◽  
General Game

scholarly journals Deep Reinforcement Learning for General Game Playing

2020 ◽  
Vol 34 (02) ◽  
pp. 1701-1708
Author(s):  
Adrian Goldwaser ◽  
Michael Thielscher
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Game Playing ◽  
Formal Description ◽  
General Game Playing ◽  
Board Games ◽  
Zero Sum ◽  
Work Done ◽  
General Game ◽  
Rules Of The Game

General Game Playing agents are required to play games they have never seen before simply by looking at a formal description of the rules of the game at runtime. Previous successful agents have been based on search with generic heuristics, with almost no work done into using machine learning. Recent advances in deep reinforcement learning have shown it to be successful in some two-player zero-sum board games such as Chess and Go. This work applies deep reinforcement learning to General Game Playing, extending the AlphaZero algorithm and finds that it can provide competitive results.

scholarly journals The International 
General Game Playing Competition

AI Magazine ◽  
2013 ◽  
Vol 34 (2) ◽  
pp. 107 ◽  
Author(s):  
Michael Genesereth ◽  
Yngvi Björnsson
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
General Problem ◽  
Game Playing ◽  
Test Bed ◽  
General Game Playing ◽  
Research Challenges ◽  
History Of ◽  
World Class ◽  
General Game

Games have played a prominent role as a test-bed for advancements in the field of Artificial Intelligence ever since its foundation over half a century ago, resulting in highly specialized world-class game-playing systems being developed for various games. The establishment of the International General Game Playing Competition in 2005, however, resulted in a renewed interest in more general problem solving approaches to game playing. In general game playing (GGP) the goal is to create game-playing systems that autonomously learn how to skillfully play a wide variety of games, given only the descriptions of the game rules. In this paper we review the history of the competition, discuss progress made so far, and list outstanding research challenges.

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