Proposes HAD-MFC framework that decouples upper-level vulnerable agent selection from lower-level adversarial policy learning in large-scale MARL using Fenchel-Rockafellar transform and MDP reformulation with provable optimality preservation.
Efficient model-based multi-agent mean- field reinforcement learning
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The authors propose actor-critic q-learning algorithms for mean-field control with common noise based on martingale orthogonality conditions and relaxed controls, establish convergence of inner iterations in the linear-quadratic case, and demonstrate performance on examples.
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Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning
Proposes HAD-MFC framework that decouples upper-level vulnerable agent selection from lower-level adversarial policy learning in large-scale MARL using Fenchel-Rockafellar transform and MDP reformulation with provable optimality preservation.
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Continuous-time q-learning for mean-field control with common noise, part-II: q-learning algorithms
The authors propose actor-critic q-learning algorithms for mean-field control with common noise based on martingale orthogonality conditions and relaxed controls, establish convergence of inner iterations in the linear-quadratic case, and demonstrate performance on examples.