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A Guided Genetic Algorithm for the Planning in Lunar Lander Games

We propose a guided genetic algorithm (GA) for planning in games. In guided GA, an extra reinforcement component is inserted into the evolution procedure of GA. During each evolution procedure, the reinforcement component will simulate the execution of a series of actions of an individual before the real trial and adjust the series of actions according to the reinforcement thus try to improve the performance. We then apply it to a Lunar Lander game in which the falling lunar module needs to learn to land on a platform safely. We compare the performance of guided GA and general GA as well as Q-Learning on the game. The result shows that the guided GA could guarantee to reach the goal and achieve much higher performance than general GA and Q-Learning.