ModellingUS & West
Stochastic MPC robust to errors in disturbance statistics
DT.ForecastarXiv — Systems and Control (eess.SY) ↗
Shared Control Between DRL and MPC for Multi-Class Transport Networks
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
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