The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Connectionist learning of expert preferences by comparison training
Advances in neural information processing systems 1
Practical Issues in Temporal Difference Learning
Machine Learning
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Learning to Play Chess Using Temporal Differences
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
A Neural Network that Learns to Play Five-in-a-Row
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
A neighboring optimal adaptive critic for missile guidance
Mathematical and Computer Modelling: An International Journal
The optimal control of discrete-time delay nonlinear system with dual heuristic dynamic programming
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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In this paper adaptive dynamic programming (ADP) is applied to learn to play Gomoku. The critic network is used to evaluate board situations. The basic idea is to penalize the last move taken by the loser and reward the last move selected by the winner at the end of a game. The results show that the presented program is able to improve its performance by playing against itself and has approached the candidate level of a commercial Gomoku program called 5-star Gomoku. We also examined the influence of two methods for generating games: self-teaching and learning through watching two experts playing against each other and presented the comparison results and reasons.