mlsanityData to 14 Oct 2026

Emergency Preemption Without Online Exploration: A Decision Transformer Approach

arXiv 2603.22315 ↗First posted 20 Mar 2026cs.LG cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

Emergency vehicle (EV) response time is a critical determinant of survival outcomes, yet deployed signal preemption strategies remain reactive and uncontrollable. We propose a return-conditioned framework for emergency corridor optimization based on the Decision Transformer (DT). By casting corridor optimization as offline, return-conditioned sequence modeling, our approach (1) eliminates online environment interaction during policy learning, (2) enables dispatch-level urgency control through a single target-return scalar, and (3) extends to multi-agent settings via a Multi-Agent Decision Transformer (MADT) with graph attention for spatial coordination. On the LightSim simulator, DT reduces average EV travel time by 37.7% relative to fixed-timing preemption on a 4x4 grid (88.6 s vs. 142.3 s), achieving the lowest civilian delay (11.3 s/veh) and fewest EV stops (1.2) among all methods, including online RL baselines that require environment interaction. MADT further improves on larger grids, overtaking DT with 45.2% reduction on 8x8 via graph-attention coordination. Return conditioning produces a smooth dispatch interface: varying the target return from 100 to -400 trades EV travel time (72.4-138.2 s) against civilian delay (16.8-5.4 s/veh), requiring no retraining. A Constrained DT extension adds explicit civilian disruption budgets as a second control knob.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 30 of its 66 references that are in the map. First posted on arXiv 20 Mar 2026.

Builds on

30 of 66 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control10 Nov
  2. 2023Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach12 Dec

Other references · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  4. 2017Proximal Policy Optimization Algorithmscited by 15,215
  5. 2017Graph Attention Networkscited by 8,923
  6. 2016Layer Normalizationcited by 5,669
  7. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  8. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  9. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  10. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  11. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,293
  12. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
16 more
  1. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  2. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 644
  3. 2017Safe Reinforcement Learning via Shieldingcited by 449
  4. 2018Reward Constrained Policy Optimizationcited by 417
  5. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  6. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 181
  7. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
  8. 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 98
  9. 2019Learning Phase Competition for Traffic Signal Controlcited by 87
  10. 2023LLMLight: Large Language Models as Traffic Signal Control Agentscited by 73
  11. 2023Constrained Decision Transformer for Offline Safe Reinforcement Learningcited by 69
  12. 2024X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learnercited by 10
  13. 2021A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehiclescited by 9
  14. 2022Learning Eco-Driving Strategies at Signalized Intersectionscited by 9
  15. 2022EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control Systemcited by 9
  16. 2023DataLight: Offline Data-Driven Traffic Signal Controlcited by 2

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  2. 2026Hierarchical GNN-Based Multi-Agent Learning for Dynamic Queue-Jump Lane and Emergency Vehicle Corridor Formation
  3. 2026SIREN-Bench: Behavior-Driven Generation and Evaluation of Emergency-Vehicle Interactions ↗
  4. 2026Spatiotemporal Decision Transformer for Traffic Coordination
  5. 2026Distributed Real-Time Vehicle Control for Emergency Vehicle Transit: A Scalable Cooperative Method
  6. 2026Dynamic Deployment of Mobile Charging Trucks During Natural Disaster Evacuation: An Offline-to-Online Framework ↗
  7. 2024Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles
  8. 2025OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities