June 11, 2024, 4:46 a.m. | Xinqiang Yu, Chuanguang Yang, Chengqing Yu, Libo Huang, Zhulin An, Yongjun Xu

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05488v1 Announce Type: new
Abstract: Policy Distillation (PD) has become an effective method to improve deep reinforcement learning tasks. The core idea of PD is to distill policy knowledge from a teacher agent to a student agent. However, the teacher-student framework requires a well-trained teacher model which is computationally expensive.In the light of online knowledge distillation, we study the knowledge transfer between different policies that can learn diverse knowledge from the same environment.In this work, we propose Online Policy Distillation …

abstract agent arxiv attention become core cs.ai cs.lg decision distillation framework however knowledge light policy reinforcement reinforcement learning tasks type

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