Fuzzy Kanerva-based function approximation for reinforcement learning
Cheng Feng Wu, Waleed M. Meleis · 2009
Radial Basis Functions and Kanerva Coding can give poor performance when applied to large-scale multi-agent systems. In this paper, we attempt to solve a collection of predator-prey pursuit instances and argue that the poor performance is caused by frequent prototype collisions. We show that dynamic prototype allocation and adaptation can give better results by reducing these collisions. We then describe our novel approach, fuzzy Kanerva-based function approximation, that uses a fine-grained fuzzy membership grade to describe a state-action pair's adjacency with respect to each prototype. This approach completely eliminates prototype collisions. We conclude that adaptive fuzzy Kanerva Coding can significantly improve a reinforcement learner's ability to solve large-scale multi-agent problems.