REPAINT: Knowledge Transfer in Deep Reinforcement Learning

Autor:	Tao, Yunzhe, Genc, Sahika, Chung, Jonathan, Sun, Tao, Mallya, Sunil
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low. This work proposes REPresentation And INstance Transfer (REPAINT) algorithm for knowledge transfer in deep reinforcement learning. REPAINT not only transfers the representation of a pre-trained teacher policy in the on-policy learning, but also uses an advantage-based experience selection approach to transfer useful samples collected following the teacher policy in the off-policy learning. Our experimental results on several benchmark tasks show that REPAINT significantly reduces the total training time in generic cases of task similarity. In particular, when the source tasks are dissimilar to, or sub-tasks of, the target tasks, REPAINT outperforms other baselines in both training-time reduction and asymptotic performance of return scores. Comment: Published at ICML 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.11827 Zobrazit plný text záznamu View this record from Arxiv