Learning to Play Games Using a PSO-Based Competitive Learning Approach
Leon Messerschmidt, Andries Petrus Engelbrecht · IEEE Transactions on Evolutionary Computation · 2004
A new competitive approach is developed for learning agents to play two-agent games. This approach uses particle swarm optimizers (PSO) to train neural networks to predict the desirability of states in the leaf nodes of a game tree. The new approach is applied to the TicTacToe game, and compared with the performance of an evolutionary approach. A performance criterion is defined to quantify performance against that of players making random moves. The results show that the new PSO-based approach performs well as compared with the evolutionary approach.