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AAMAS
2007
Springer
15 years 6 months ago
Shaping multi-agent systems with gradient reinforcement learning
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...
Olivier Buffet, Alain Dutech, François Char...
AAMAS
2005
Springer
15 years 6 months ago
Coordinating Multiple Agents via Reinforcement Learning
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...
Gang Chen, Zhonghua Yang, Hao He, Kiah Mok Goh
EVOW
2003
Springer
15 years 11 months ago
Exploring the T-Maze: Evolving Learning-Like Robot Behaviors Using CTRNNs
Abstract. This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double ...
Jesper Blynel, Dario Floreano
AUSAI
2008
Springer
15 years 8 months ago
Partial Order Hierarchical Reinforcement Learning
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...
Bernhard Hengst
IJCM
2008
93views more  IJCM 2008»
15 years 6 months ago
A reinforced learning control using iterative error compensation for uncertain dynamical systems
This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of high speed, computer controlled machining process. ...
Kuei-Shu Hsu, Wen-Shyong Yu, Ming-In Ho