|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'501'711
Articles rated: 2609
20 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Monte Carlo Q-learning for General Game Playing | | Hui Wang
; Michael Emmerich
; Aske Plaat
; | | Date: |
Fri, 16 Feb 2018 14:18:46 GMT (1865kb,D) | | 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 & 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. | | Source: | arXiv, 1802.5944 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER