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Learning Progress Driven Multi-Agent Curriculum
[article]
2024
arXiv
pre-print
Curriculum reinforcement learning (CRL) aims to speed up learning by gradually increasing the difficulty of a task, usually quantified by the achievable expected return. Inspired by the success of CRL in single-agent settings, a few works have attempted to apply CRL to multi-agent reinforcement learning (MARL) using the number of agents to control task difficulty. However, existing works typically use manually defined curricula such as a linear scheme. In this paper, we first apply
arXiv:2205.10016v2
fatcat:ryzq7ehhtva27cd223hvdokeaa