Multitask Reinforcement Learning by Optimizing Neural Pathways

Samin Yeasar Arnob, Riyasat Ohib, Amy Zhang, Sergey Plis, Doina Precup

Keywords: Multitask Learning, Online Reinforcement Learning, Offline Reinforcement Learning, Neural Pathways

TL;DR: Proposing a novel multitask learning framework using task-specific neural pathways for online and offline reinforcement learning.

Abstract: Reinforcement learning (RL) algorithms have achieved great success in learning specific tasks, as evidenced by examples such as AlphaGo or fusion control. However, it is still difficult for an RL agent to learn how to solve multiple tasks. In this paper, we propose a novel multitask learning framework, in which multiple specialized pathways through a single network are trained simultaneously, with each pathway focusing on a single task. We show that this approach achieves competitive performance with existing multitask RL methods, while using only 5% of the number of neurons per task. We demonstrate empirically the success of our approach on several continuous control tasks, in both online and offline training.

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Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)