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Shared Control Between DRL and MPC for Multi-Class Transport Networks

US & West · 24 Aug

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.

Relevant to DT products

Original headline: Sharing the Control Authority Between Deep Reinforcement Learning and Model Predictive Control: Application to Multi-Class Transportation Networks

Read the original: arXiv — Systems and Control (eess.SY)

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