Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
By Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye. 2020
56 citations recorded by the source corpus.
While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks. In this paper, we present a Knowledge Transfer based Multi-task Deep Reinforcement Learning framework (KTM-DRL) for continuous control, which enables a single DRL agent to achieve expert-level performance in multiple different tasks by learning from task-specific teachers. In KTM-DRL, the multi-task agent first leverages an offline knowledge transfer algorithm designed particularly for the actor-critic architecture to quickly learn a control policy from the experience of task-specific teachers, and then it employs an online learning algorithm to further improve itself by learning from new online transition samples under the guidance of those teachers. We perform a comprehensive empirical study with two commonly-used benchmarks in the MuJoCo continuous control task suite. The experimental results well justify the effectiveness of KTM-DRL and its knowledge transfer and online learning algorithms, as well as its superiority over the state-of-the-art by a large margin.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
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In Artificial Intelligence
- Dacheng Tao
- Jianping Gou
- S. Maybank
- Baosheng Yu
- Gabriella Csurka
- Jing Zhang
- Charu C. Aggarwal
- Dan Alistarh
In Computational Theory and Mathematics
Citations in the corpus
- Knowledge Distillation: A Survey (2020, background): “…such as the adversarial learning (Liu et al. 2018), auto machine learning (Macko et al. 2019; Fakoor et al. 2020), label noise filtering learning (Xia et al. 2018), lifelong learning (Zhai et al. 2019), and reinforcement learning (Ashok et al. 2018; Xu et al. 2020c; Zhao and Hospedales 2020).”