Brains, Behavior and Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Automatic basis function construction for approximate dynamic programming and reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Adaptive Kanerva-based function approximation for multi-agent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Adaptive Fuzzy Function Approximation for Multi-agent Reinforcement Learning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
| Hi-index | 0.00 |
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.