Decentralized Reinforcement Learning Control of a Robotic Manipulator

— Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, etc. Learning approaches to multi-agent control, many of them based on reinforcement learning (RL), are investigated in complex domains such as teams of mobile robots. However, the application of decentralized RL to low-level control tasks is not as intensively studied. In this paper, we investigate centralized and decentralized RL, emphasizing the challenges and potential advantages of the latter. These are then illustrated on an example: learning to control a two-link rigid manipulator. Some open issues and future research directions in decentralized RL are outlined. Keywords—multi-agent learning, decentralized control, reinforcement learning

• Type: Conference
• Year: 2006
• Where: ICARCV
• Authors: Lucian Busoniu, Bart De Schutter, Robert Babuska
• Keywords: Decentralized Rl, ICARCV 2006, Low-level Control Tasks, Reinforcement Learning