Name
Abstract | ||
|---|---|---|
Recently, the interest in reinforcement learning in game playing has been renewed. This is evidenced by the groundbreaking results achieved by AlphaGo. General Game Playing (GGP) provides a good testbed for reinforcement learning, currently one of the hottest fields of AI. In GGP, a specification of games rules is given. The description specifies a reinforcement learning problem, leaving programs to find strategies for playing well. Q-learning is one of the canonical reinforcement learning methods, which is used as baseline on some previous work (Banerjee u0026 Stone, IJCAI 2007). We implement Q-learning in GGP for three small board games (Tic-Tac-Toe, Connect-Four, Hex). We find that Q-learning converges, and thus that this general reinforcement learning method is indeed applicable to General Game Playing. However, convergence is slow, in comparison to MCTS (a reinforcement learning method reported to achieve good results). We enhance Q-learning with Monte Carlo Search. This enhancement improves performance of pure Q-learning, although it does not yet out-perform MCTS. Future work is needed into the relation between MCTS and Q-learning, and on larger problem instances. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Convergence (routing),Monte Carlo method,Computer science,Q-learning,Testbed,Artificial intelligence,General game playing,Game playing,Machine learning,Reinforcement learning |
DocType | Volume | Citations |
Journal | abs/1802.05944 | 2 |
PageRank | References | Authors |
0.37 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Wang | 1 | 340 | 59.46 |
| Michael Emmerich | 2 | 1243 | 71.89 |
| Aske Plaat | 3 | 524 | 72.18 |