The paper deals with instance-based reinforcement learning control of autonomous robots. A classifier system, defined in the continuous state and action spaces, is outlined. Based on the sensory state space analysis, we define a learning strategy and fix structure of the action rules. The classifier system features a nonconservative bucket brigade algorithm and a fast reproduction mechanism. The system developed is then applied to learning cooperative behavior by two robots coupled via a common object, with each robot controlled by its own classifier. Feasibility of this scheme is tested under experiment with two Lynxmotion robots, and a motion pattern of cooperative behavior (lifting up an object) is evolved using the two interacting classifier systems.
|Number of pages||7|
|Journal||IEEE International Conference on Intelligent Robots and Systems|
|Publication status||Published - Jan 1 2000|
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications