Reinforcement Learning in Video Games Using Nearest Neighbor Interpolation and Metric Learning
Reinforcement learning (RL) has had mixed success when applied to games. Large state spaces and the curse of dimensionality have limited the ability for RL techniques to learn to play complex games in a reasonable length of time. We discuss a modification of Q-learning to use nearest neighbor states to exploit previous experience in the early stages of learning. A weighting on the state features is learned using metric learning techniques, such that neighboring states represent similar game situations. Our method is tested on the arcade game Frogger, and it is shown that some of the effects of the curse of dimensionality can be mitigated.