Temporal difference learning and TD-Gammon
Communications of the ACM
On verifying game designs and playing strategies using reinforcement learning
Proceedings of the 2001 ACM symposium on Applied computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Interactive Verification of Game Design and Playing Strategies
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Player Co-Modelling in a Strategy Board Game: Discovering How to Play Fast
Cybernetics and Systems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
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We review an experiment in co-evolutionary learning of game playing where we show experimental evidence that the straightforward composition of individually learned models more often than not results in diluting what was earlier learned and that self-playing can result in reaching plateaus of un-interesting playing behavior These observations suggest that learning cannot be easily distributed when one hopes to harness multiple experts to develop a quality computer player and reinforce the need to develop tools that facilitate the mix of expert-based tuition and computer self-learning.