Practical Issues in Temporal Difference Learning
Machine Learning
Temporal difference learning and TD-Gammon
Communications of the ACM
Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
M2ICAL: A Tool for Analyzing Imperfect Comparison Algorithms
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Reinforcement learning for games: failures and successes
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
GP-EndChess: using genetic programming to evolve chess endgame players
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
GP-Gammon: using genetic programming to evolve backgammon players
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Evolving neural networks to play checkers without relying on expert knowledge
IEEE Transactions on Neural Networks
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Artificial neural networks have been successfully used as approximating value functions for tasks involving decision making. In domains where a shift in judgment for decisions is necessary as the overall state changes, it is hypothesized that multiple neural networks are likely be beneficial as an approximation of a value function over those that employ a single network. For our experiments, the card game Dominion was chosen as the domain. This work compares neural networks generated by various machine learning methods successfully applied to other games (such as in TD-Gammon) to a genetic algorithm method for generating two neural networks for different phases of the game along with evolving a transition point. The results demonstrate a greater success ratio with the method hypothesized. This suggests future work examining more complex multiple neural network configurations could apply to this game domain as well as being applicable to other problems.