CIS Publication Spotlight [Publication Spotlight]
Digital Object Identifier: 10.1109/ TNNLS.2012.2210559 “A methodology for quickly learning to play games at a strong level is developed. The methodology consists of a novel combination of three techniques, and a variety of experiments on the game of Othello demonstrates their usefulness. Structures or topologies in neural network connectivity patterns are used to decrease the number of learning parameters and to deal more effectively with the structural credit assignment problem, which is to change individual network weights based on the obtained feedback. Furthermore, the structured neural networks are trained with the novel neural-fitted temporal difference (TD) learning algorithm to create a system that can exploit most of the training exper iences and enhance learning speed and performance. Finally, the neural-fitted TD-leaf algorithm is used to learn more effectively when lookahead search is performed by the game-playing program. Extensive experimental study indicated that the proposed method outperforms linear networks and fully connected neural networks or evaluation functions evolved with evolutionary algorithms.”