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BERTINORO
2005
Springer

Emergent Consensus in Decentralised Systems Using Collaborative Reinforcement Learning

12 years 7 months ago
Emergent Consensus in Decentralised Systems Using Collaborative Reinforcement Learning
Abstract. This paper describes the application of a decentralised coordination algorithm, called Collaborative Reinforcement Learning (CRL), to two different distributed system problems. CRL enables the establishment of consensus between independent agents to support the optimisation of system-wide properties in distributed systems where there is no support for global state. Consensus between interacting agents on local environmental or system properties is established through localised advertisement of policy information by agents and the use of advertisements by agents to update their local, partial view of the system. As CRL assumes homogeneity in advertisement evaluation by agents, advertisements that improve the system optimisation problem tend to be propagated quickly through the system, enabling the system to collectively adapt its behaviour to a changing environment. In this paper, we describe the application of CRL to two different distributed system problems, a routing prot...
Jim Dowling, Raymond Cunningham, Anthony Harringto
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where BERTINORO
Authors Jim Dowling, Raymond Cunningham, Anthony Harrington, Eoin Curran, Vinny Cahill
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