Reinforcement of Local Pattern Cases for Playing Tetris.
Houcine Romdhane, Luc Lamontagne · 2008
In the paper, we investigate the use of reinforcement learn-ing in CBR for estimating and managing a legacy case base for playing the game of Tetris. Each case corresponds to a local pattern describing the relative height of a subset of columns where pieces could be placed. We evaluate these patterns through reinforcement learning to determine if sig-nificant performance improvement can be observed. For es-timating the values of the patterns, we compare Q-learning with a simpler temporal difference formulation. Our results indicate that training without discounting provides slightly better results than other evaluation schemes. We also ex-plore how the reinforcement values of the patterns can help reduce the size of the case base. We report on experiments we conducted for forgetting cases.. 1.