A New Framework for Multi-Agent Reinforcement Learning - Centralized Training and Exploration with Decentralized Execution via Policy Distillation

Gang Chen 0002. A New Framework for Multi-Agent Reinforcement Learning - Centralized Training and Exploration with Decentralized Execution via Policy Distillation. In Amal El Fallah-Seghrouchni, Gita Sukthankar, Bo An 0001, Neil Yorke-Smith, editors, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020. pages 1801-1803, International Foundation for Autonomous Agents and Multiagent Systems, 2020. [doi]

@inproceedings{000220,
  title = {A New Framework for Multi-Agent Reinforcement Learning - Centralized Training and Exploration with Decentralized Execution via Policy Distillation},
  author = {Gang Chen 0002},
  year = {2020},
  url = {https://dl.acm.org/doi/abs/10.5555/3398761.3398987},
  researchr = {https://researchr.org/publication/000220},
  cites = {0},
  citedby = {0},
  pages = {1801-1803},
  booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 2020},
  editor = {Amal El Fallah-Seghrouchni and Gita Sukthankar and Bo An 0001 and Neil Yorke-Smith},
  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
  isbn = {978-1-4503-7518-4},
}