mlsanityData to 14 Oct 2026

SelectLight: Learning to Select Signal Plans Generated by Distributed Model Predictive Control for Urban Traffic Networks

arXiv 2608.12256 ↗First posted 12 Aug 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Coordinated traffic signal control across urban networks must adapt to changing demand while satisfying operational constraints. Multi-objective distributed model predictive control (DMPC) can construct feasible signal plans online, but prescribed rules for selecting among trade-off solutions cannot learn from realized closed-loop outcomes. We propose SelectLight, which implements post-optimization selection by allowing a multi-agent reinforcement learning (MARL) policy to choose directly from plans generated online by DMPC. At each control update, state-pruned multi-objective dynamic programming (SP-MODP) evaluates plans with a Newellian point--spatial queue model and returns a bounded set of mutually nondominated candidate signal plans for total queueing delay, peak queue accumulation, and total number of stops. A topology-aware attention policy trained with independent proximal policy optimization (IPPO) selects one unmodified plan from each variable-size set. This confines learning to candidate selection, preserves the prescribed signal timing constraints, and leaves the selected plan and its predicted objective trade-offs available for inspection. Experiments on two 28-intersection SUMO networks show that SelectLight achieves the best delay-related performance and that its advantage widens with demand. At twice the baseline demand, it reduces queueing delay and waiting time by 5.57% and 6.44%, respectively, relative to the strongest baseline. SelectLight also incurs the lowest transfer loss under every tested demand shift. With a 120 s prediction horizon, the per-intersection 99th-percentile SP-MODP solution time is 5.408 ms, well below the 5 s control interval.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 68 references that are in the map. First posted on arXiv 12 Aug 2026.

Builds on

16 of 68 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification4 Feb
  2. 2024A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control4 Feb

Other references · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  4. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 294
  5. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
  6. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
  7. 2016Learning values across many orders of magnitudecited by 85
  8. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 68
  9. 2019MPC-Net: A First Principles Guided Policy Searchcited by 34
  10. 2023Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Controlcited by 6
  11. 2022A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Controlcited by 2
  12. 2025CV-MP: Max-Pressure Control in Heterogeneously Distributed and Partially Connected Vehicle Environmentscited by 2
2 more
  1. 2024Connected Vehicle Data-driven Robust Optimization for Traffic Signal Timing: Modeling Traffic Flow Variability and Errorscited by 1
  2. 2024A Probabilistic Approach for Queue Length Estimation Using License Plate Recognition Data: Considering Overtaking in Multi-lane Scenarioscited by 1

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  1. 2025A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
  2. 2026CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
  3. 2025Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
  4. 2025Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse
  5. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  6. 2025Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control ↗
  7. 2025Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control
  8. 2025Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths