Differentiable Policy Transport over Multi-Layer Network Feasibility Geometry
Network Feasibility Geometry Reinforcement Learning (NFG-RL) is proposed, which models coupled constraints via transport theory and the residual inclusion of $\bphi_{\mathfrak{N}}(x,a)\in\cK_{\mathfrak{N}}$, defining the executed policy as the pushforward of a proto-policy through a feasibility-transport map.
Zu-Yuan Zhang, Ze-Yu Fang, Mahdi Imani et al.
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