Apply ant colony optimization to Tetris

Authors:
Xingguo Chen;Hao Wang;Weiwei Wang;Yinghuan Shi;Yang Gao
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing , China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing , China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 4
Cited 0

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Abstract

Tetris is a falling block game where the player's objective is to arrange a sequence of different shaped tetrominoes smoothly in order to survive. In the intelligence games, agent imitates the real player and chooses the best move based on a linear value function. In this paper, we apply Ant Colony Optimization (ACO) method to learn the weights of the function, trying to search an optimal weight-path in the weight graph. We use dynamic heuristic to prevent premature convergence to local optima. Our experimental result is better than most of traditional reinforcement learning methods.