Feb. 13, 2024, 5:44 a.m. | Yasin Findik S. Reza Ahmadzadeh

cs.LG updates on arXiv.org arxiv.org

Tackling multi-agent learning problems efficiently is a challenging task in continuous action domains. While value-based algorithms excel in sample efficiency when applied to discrete action domains, they are usually inefficient when dealing with continuous actions. Policy-based algorithms, on the other hand, attempt to address this challenge by leveraging critic networks for guiding the learning process and stabilizing the gradient estimation. The limitations in the estimation of true return and falling into local optima in these methods result in inefficient and …

agent algorithms challenge continuous cs.lg cs.ma cs.ro domains efficiency excel mixed multi-agent multi-agent learning policy sample value

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