Temporal difference learning and TD-Gammon
Communications of the ACM
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On Pruning Techniques for Multi-Player Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Two Search Techniques for Imperfect Information Games and Application to Hearts
Two Search Techniques for Imperfect Information Games and Application to Hearts
Multiplayer games: algorithms and approaches
Multiplayer games: algorithms and approaches
Robust game play against unknown opponents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
An Analysis of UCT in Multi-player Games
CG '08 Proceedings of the 6th international conference on Computers and Games
Fast gradient-descent methods for temporal-difference learning with linear function approximation
ICML '09 Proceedings of the 26th Annual 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
Improving state evaluation, inference, and search in trick-based card games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Automated discovery of search-extension features
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
On the complexity of trick-taking card games
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Temporal difference (TD) learning has been used to learn strong evaluation functions in a variety of two-player games. TD-gammon illustrated how the combination of game tree search and learning methods can achieve grand-master level play in backgammon. In this work, we develop a player for the game of hearts, a 4-player game, based on stochastic linear regression and TD learning. Using a small set of basic game features we exhaustively combined features into a more expressive representation of the game state. We report initial results on learning with various combinations of features and training under self-play and against search-based players. Our simple learner was able to beat one of the best search-based hearts programs.