Temporal difference learning and TD-Gammon
Communications of the ACM
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Reinforcement learning of local shape in the game of go
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Sample-based learning and search with permanent and transient memories
Proceedings of the 25th international conference on Machine learning
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
On-line learning: where are we so far?
Ubiquitous knowledge discovery
On-line learning: where are we so far?
Ubiquitous knowledge discovery
Learning to win by reading manuals in a monte-carlo framework
Journal of Artificial Intelligence Research
Social signal and user adaptation in reinforcement learning-based dialogue management
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
Reinforcement learning in robotics: A survey
International Journal of Robotics Research
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It is often thought that learning algorithms that track the best solution, as opposed to converging to it, are important only on nonstationary problems. We present three results suggesting that this is not so. First we illustrate in a simple concrete example, the Black and White problem, that tracking can perform better than any converging algorithm on a stationary problem. Second, we show the same point on a larger, more realistic problem, an application of temporal difference learning to computer Go. Our third result suggests that tracking in stationary problems could be important for metalearning research (e.g., learning to learn, feature selection, transfer). We apply a metalearning algorithm for step-size adaptation, IDBD (Sutton, 1992a), to the Black and White problem, showing that meta-learning has a dramatic long-term effect on performance whereas, on an analogous converging problem, meta-learning has only a small second-order effect. This small result suggests a way of eventually overcoming a major obstacle to meta-learning research: the lack of an independent methodology for task selection.