MARL produces transferable rendezvous strategies in vortical flows that outperform naive navigation by exploiting fluid kinematics to prevent agents from becoming trapped in separate vortices.
arXiv preprint arXiv:2105.08268 (2021)
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A monograph develops the probabilistic and control-theoretic framework connecting multi-agent reinforcement learning to mean field control, including analyses of Q-learning, policy gradients, and numerical methods for linear-quadratic and general models.
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Multi-agent rendezvous in fluid flows via reinforcement learning
MARL produces transferable rendezvous strategies in vortical flows that outperform naive navigation by exploiting fluid kinematics to prevent agents from becoming trapped in separate vortices.
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Mean Field Reinforcement Learning
A monograph develops the probabilistic and control-theoretic framework connecting multi-agent reinforcement learning to mean field control, including analyses of Q-learning, policy gradients, and numerical methods for linear-quadratic and general models.