Title :
An Interconnected Dynamical System composed of dynamics-based Reinforcement Learning agents in a distributed environment: A case study
Author :
Madera, Manuel ; Megherbi, D.B.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Massachusetts, Lowell, MA, USA
Abstract :
This paper presents a case study of an Interconnected Dynamical System (IDS) composed of Intelligent Reinforcement Learning (RL) agents, and characterized by a Hybrid P2P/Master-Slave architecture. In particular, we propose and extent our previously proposed non-dynamics-based RL work to make it an IDS. Furthermore, we study how the addition of motion constrains, knowledge sharing between agents, and distributed computing affect the overall performance of the system. In addition, we introduce a new dynamics based reward mechanism for reinforcement learning agents.
Keywords :
learning (artificial intelligence); peer-to-peer computing; IDS; RL; distributed computing; distributed environment:; dynamics-based reinforcement learning agents; hybrid P2P-master-slave architecture; interconnected dynamical system; nondynamics-based RL work; Equations; Heuristic algorithms; Learning; Mathematical model; Nickel; Turning; Vectors; Distributed multi-agent systems; Interconnected Dynamical Systems; Multi-agent systems; Reinforcement Learning; dynamics based reinforcement learning;
Conference_Titel :
Computational Intelligence for Measurement Systems and Applications (CIMSA), 2012 IEEE International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4577-1778-9
DOI :
10.1109/CIMSA.2012.6269597
