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What is Mean Field MARL: Theory and Practice about?

This paper introduces Mean Field Reinforcement Learning (MFRL) to address the challenges of multi-agent reinforcement learning (MARL) when the number of agents is large, which typically leads to intractable learning due to increased dimensionality and interactions. MFRL approximates interactions within a population of agents by considering the average effect on a single agent, allowing for the development of scalable algorithms such as mean field Q-learning and mean field Actor-Critic. The effectiveness of

Author

Tran Quang Anh

Language

EN