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arXiv · 1802.01034

Multi-task Learning for Continuous Control

Himani Arora·Rajath Kumar·Jason Krone·Chong Li
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Abstract

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.

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Himani Arora, Rajath Kumar, Jason Krone, Chong Li. 2018-02-03. Multi-task Learning for Continuous Control. https://arxiv.org/abs/1802.01034

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