Apply ant colony optimization to Tetris

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. Categories and Subject Descriptors: G.3 [PROBABILITY AND STATISTICS]: Markov processes General Terms: Experimentation

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Xingguo Chen, Hao Wang, Weiwei Wang, Yinghuan Shi, Yang Gao
• Keywords: Ant Colony Optimization, Artificial Intelligence, Different Shaped Tetrominoes, GECCO 2009, Linear Value Function