Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969624 | Pattern Recognition | 2017 | 34 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Yusen Zhan, Haitham Bou Ammar, Matthew E. Taylor,