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Shared Control Between DRL and MPC for Multi-Class Transport Networks
US & West ·
The paper discusses sharing control authority between deep reinforcement learning (DRL) and model predictive control (MPC) for multi-class transportation networks. It aims to overcome limitations of each method: DRL's learning capacity in high-dimensional state spaces and MPC's computational cost.
Why it matters
This work is important for developing scalable and efficient control methods for complex urban transportation systems.
arXiv:2610.28907v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) offers a principled way of populating simulated economies with many heterogeneous decision makers.
arXiv:2604.04455v3 Announce Type: replace Abstract: Nonlinear underactuated systems such as two-wheeled inverted pendulums (TWIPs) exhibit a limited region of attraction (RoA), which defines the set of initial conditions from which the closed-loop system converges to the equilibrium.
The same TAdviser item: the generative AI market reached 58 billion rubles in 2025, with 44 billion corporate. Measurable results are becoming the key criterion, while most projects remain pilots due to integration, data quality, security and cost issues.
