An original Reinforcement Learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task o...
In this paper, we focus on the coordination issues in a multiagent setting. Two coordination algorithms based on reinforcement learning are presented and theoretically analyzed. O...
In this paper the notion of a partial-order plan is extended to task-hierarchies. We introduce the concept of a partial-order taskhierarchy that decomposes a problem using multi-ta...
This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of high speed, computer controlled machining process. ...
We present a system for visual robotic docking using an omnidirectional camera coupled with the actor critic reinforcement learning algorithm. The system enables a PeopleBot robot...