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Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi-Agent Systems

Research Area:  Machine Learning


Learning-based approaches to autonomic systems allow systems to adapt their behaviour to best suit their operating environment. One of the more widely used learning methods is Reinforcement Learning (RL). RL agents learn by repeatedly executing actions and observing their results, and over time a representation of how to behave well is developed. A significant issue with this approach is that it takes a long time to reach its best performance. Every action has to be experienced several times in each particular circumstance for its value to be representative of its converged value. The presence of multiple agents increases the number of experiences required.Multi-Agent Systems (MAS) are systems in which multiple agents affect a shared environment. MAS are inherently large-scale as they are composed of many interacting agents. In MAS, the cumulative effects of agents actions make the environment more variable. Greater variability in the outcomes of actions requires more learning as a single sample becomes less representative of the converged value. An RL systems performance is necessarily sub-optimal while it is learning. Each learning experience is expensive,taking time and affecting the system that is being controlled. Experiences should be used as efficiently as possible to reduce the time spent learning, improving overall performance.The less time needed to learn, the better the performance of a system over its lifetime.

Name of the Researcher:  Adam Taylor

Name of the Supervisor(s):  

Year of Completion:  2016

University:   University of Dublin

Thesis Link:   Home Page Url