Multi-task Learning for Continuous Control

Reliable and effective multi-task learning is a prerequisite for the development of robotic agents that can quickly learn to accomplish related, everyday tasks. However, in the reinforcement learning domain, multi-task learning has not exhibited the same level of success as in other domains, such as...

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Bibliographic Details
Published in:arXiv.org 2018-02
Main Authors: Arora, Himani, Kumar, Rajath, Krone, Jason, Li, Chong
Format: Article
Language:English
Subjects:
Computer simulation
Computer vision
Control tasks
Domains
Machine learning
Robot control
Online Access:Get full text
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Summary:Reliable and effective multi-task learning is a prerequisite for the development of robotic agents that can quickly learn to accomplish related, everyday tasks. However, in the reinforcement learning domain, multi-task learning has not exhibited the same level of success as in other domains, such as computer vision. In addition, most reinforcement learning research on multi-task learning has been focused on discrete action spaces, which are not used for robotic control in the real-world. In this work, we apply multi-task learning methods to continuous action spaces and benchmark their performance on a series of simulated continuous control tasks. Most notably, we show that multi-task learning outperforms our baselines and alternative knowledge sharing methods.
ISSN:2331-8422