Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Practical Issues in Temporal Difference Learning
Machine Learning
Technical Note: \cal Q-Learning
Machine Learning
The Convergence of TD(λ) for General λ
Machine Learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
A geometric framework for machine learning
A geometric framework for machine learning
Studies in artificial evolution
Studies in artificial evolution
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Temporal difference learning and TD-Gammon
Communications of the ACM
A Teaching Strategy for Memory-Based Control
Artificial Intelligence Review - Special issue on lazy learning
Multi-agent reinforcement learning in Markov games
Multi-agent reinforcement learning in Markov games
Multidimensional divide-and-conquer
Communications of the ACM
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Learning to Predict by the Methods of Temporal Differences
Machine Learning
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
Distance Metrics for Instance-Bsed Learning
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
On-Line Learning of Coordination Plans
On-Line Learning of Coordination Plans
On growing better decision trees from data
On growing better decision trees from data
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Multiresolution instance-based learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Tracking dynamic team activity
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Resolution-Based Policy Search for Imperfect Information Differential Games
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
International Journal of Systems Science
An experimental adaptive fuzzy controller for differential games
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A Reinforcement Learning Adaptive Fuzzy Controller for Differential Games
Journal of Intelligent and Robotic Systems
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Game playing has been a popular problem area for research in artificial intelligence and machine learning for many years. In almost every study of game playing and machine learning, the focus has been on games with a finite set of states and a finite set of actions. Further, most of this research has focused on a single player or team learning how to play against another player or team that is applying a fixed strategy for playing the game. In this paper, we explore multiagent learning in the context of game playing and develop algorithms for “co-learning” in which all players attempt to learn their optimal strategies simultaneously. Specifically, we address two approaches to colearning, demonstrating strong performance by a memory-based reinforcement learner and comparable but faster performance with a tree-based reinforcement learner.