Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control. (arXiv:2209.07420v2 [cs.RO] UPDATED)

In recent years, reinforcement learning and its multi-agent analogue have
achieved great success in solving various complex control problems. However,
multi-agent reinforcement learning remains challenging both in its theoretical
analysis and empirical design of algorithms, especially for large swarms of
embodied robotic agents where a definitive toolchain remains part of active
research. We use emerging state-of-the-art mean-field control techniques in
order to convert many-agent swarm control into more classical single-agent
control of distributions. This allows profiting from advances in single-agent …

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