Knowledge transfer in multi-agent reinforcement learning with incremental number of agents

Author:

Liu Wenzhang1,Dong Lu2,Liu Jian1,Sun Changyin1

Affiliation:

1. School of Automation, Southeast University,Nanjing,China,210096

2. School of Cyber Science and Engineering, Southeast University,Nanjing,China,211189

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference41 articles.

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2. OTL: a framework of online transfer learning;zhao;Proc Of the 27th International Conference on Machine Learning,0

3. A new framework for multi-agent reinforcement learning-centralized training and exploration with decentra-lized execution via policy distillation;chen;Proc of International Conference on Autonomous Agents and Multiagent Systems,0

4. Distilling policy distillation;czarnecki;Proc of the 22th International Conference on Artificial Intelligence and Statistics,0

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1. Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning;IEEE/CAA Journal of Automatica Sinica;2024-07

2. DGTRL: Deep graph transfer reinforcement learning method based on fusion of knowledge and data;Information Sciences;2024-02

3. Event-driven muti-agent evacuation based on reinforcement learning;Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022);2023-01-13

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