Temporal difference learning and TD-Gammon
Communications of the ACM
Neural Networks
Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
Solution concepts in coevolutionary algorithms
Solution concepts in coevolutionary algorithms
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
New methods for competitive coevolution
Evolutionary Computation
A theoretical and experimental account of n-tuple classifier performance
Neural Computation
Pattern recognition and reading by machine
IRE-AIEE-ACM '59 (Eastern) Papers presented at the December 1-3, 1959, eastern joint IRE-AIEE-ACM computer conference
Coevolutionary temporal difference learning for Othello
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Observing the evolution of neural networks learning to play the game of Othello
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Reinforcement learning with n-tuples on the game connect-4
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Shaping fitness function for evolutionary learning of game strategies
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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We propose Coevolutionary Gradient Search, a blueprint for a family of iterative learning algorithms that combine elements of local search and population-based search. The approach is applied to learning Othello strategies represented as n-tuple networks, using different search operators and modes of learning. We focus on the interplay between the continuous, directed, gradient-based search in the space of weights, and fitness-driven, combinatorial, coevolutionary search in the space of entire n-tuple networks. In an extensive experiment, we assess both the objective and relative performance of algorithms, concluding that the hybridization of search techniques improves the convergence. The best algorithms not only learn faster than constituent methods alone, but also produce top ranked strategies in the online Othello League.