Scalable Lifelong Reinforcement Learning

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Scalable Lifelong Reinforcement Learning

Published December 2017

Journal of Pattern Recognition, Volume, 72, Dec. 2017 

Authors: Yusen Zhan, Haitham Bou-Ammar (PROWLER.io), and Matthew E. Taylor 

Abstract: Lifelong reinforcement learning provides a successful framework for agents to learn multiple consecutive tasks sequentially. Current methods, however, suffer from scalability issues when the agent has to solve a large number of tasks. In this paper, we remedy the above drawbacks and propose a novel scalable technique for lifelong reinforcement learning. We derive an algorithm which assumes the availability of multiple processing units and computes shared repositories and local policies using only local information exchange. We then show an improvement to reach a linear convergence rate compared to current lifelong policy search methods. Finally, we evaluate our technique on a set of benchmark dynamical systems and demonstrate learning speed-ups and reduced running times.

Data Efficiency

Distributed Optimisation

Learning to Learn

Lifelong Learning

Multitask Learning

Online Learning

Reinforcement Learning

Scalability


See paper