LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning

ICLR , 2022

Recommended citation: David Henry Mguni, Taher Jafferjee, Jianhong Wang, Oliver Slumbers, Nicolas Perez-Nieves, Feifei Tong, Li Yang, Jiangcheng Zhu, Yaodong Yang, and Jun Wang. "Ligs: Learnable intrinsic-reward generation selection for multi-agent learning." arXiv preprint arXiv:2112.02618 (2021). http://academicpages.github.io/files/paper2.pdf

(ChatGPT-Generated) The paper introduces a new framework called LIGS, which improves coordination and performance of multi-agent reinforcement learners by introducing a learnable intrinsic-reward generation selection algorithm. LIGS enables systems of multi-agent reinforcement learners to quickly solve environments with sparse rewards and demonstrates superior performance in challenging tasks in Foraging and StarCraft II.

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Recommended citation: David Henry Mguni, Taher Jafferjee, Jianhong Wang, Oliver Slumbers, Nicolas Perez-Nieves, Feifei Tong, Li Yang, Jiangcheng Zhu, Yaodong Yang, and Jun Wang. “Ligs: Learnable intrinsic-reward generation selection for multi-agent learning.” arXiv preprint arXiv:2112.02618 (2021).