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Parent Message
Multi-Agent Chess
Very interesting thoughts. A few quick comments... The rule-based approach is simular to that of TD gammon, except it uses a NN to decide the move. However, it selects different features to base its decision on as it learns by playing itself repeatedly. This self-adapting mechanism is reminiscent of what you describe. The neural network approach you describe would be difficult: The multi-agent version sounds like a great idea. I was reading about some theory yesterday that could apply to this: W-Learning. Essentially, you learn to select which of the small agents (or Q-Learners) gets to move. This seems to work well for some fairly complex problems, but chess is another matter. This is not necessarily a gready approach, but I don't know whether it would learn about planning... Definitely worth experimenting upon. |
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Tutorials on the Subject
Do you know of any tutorials on the W-Learning/Q-Learners? So far, what I'm finding on Google is a little over my head. |
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State of the Art RL
They're both parts of Reinforcement Learning. Look up stuff on that in Google, or just wait a couple days... (read on ;) W-Learning is brand new -- well, almost. It started with a guy who submitted his Ph.D. in 1997, so there's only recent work on this. Q-learning is well documented, and I'm in fact there's a bit of stuff in the new Knowledge Warehouse about this; it'll hopefully see the light of day on saturday or sunday (if I can get my perl distribution working at home). So keep posted! |
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Reinforcement Learning
Thanks for the tip on Reinforement Learning. I found a couple of tutorials on the Reinforcement Learning Repository at the Univeristy of Mass. at Amherst. Some of the links are broken, but among them is a pre-print book Reinforcement Learning: An Introduction. I can't wait to see what gets add to the Knowledge Warehouse. |
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