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Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles

arXiv 1812.06120 ↗First posted 14 Dec 2018eess.SY cs.AI cs.ROcited by 19

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Abstract (arXiv)

Using deep reinforcement learning, we train control policies for autonomous vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library for deep reinforcement learning in micro-simulators, we train two policies, one policy with noise injected into the state and action space and one without any injected noise. In simulation, the autonomous vehicle learns an emergent metering behavior for both policies in which it slows to allow for smoother merging. We then directly transfer this policy without any tuning to the University of Delaware Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles. We characterize the performance of both policies on the scaled city. We show that the noise-free policy winds up crashing and only occasionally metering. However, the noise-injected policy consistently performs the metering behavior and remains collision-free, suggesting that the noise helps with the zero-shot policy transfer. Additionally, the transferred, noise-injected policy leads to a 5% reduction of average travel time and a reduction of 22% in maximum travel time in the UDSSC. Videos of the controllers can be found at https://sites.google.com/view/iccps-policy-transfer.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2018.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 9; 2025: 1.

19 papers.

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  1. 2024AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Datasetcited by 15
  2. 2022Unified Automatic Control of Vehicular Systems with Reinforcement Learningcited by 13
  3. 2023Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersectionscited by 12
  4. 2021Scalable Multiagent Driving Policies For Reducing Traffic Congestioncited by 11
  5. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policiescited by 10
  6. 2023Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomycited by 6
  7. 2024Autonomous Driving Small-Scale Cars: A Survey of Recent Developmentcited by 6
  8. 2022Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agentscited by 5
  9. 2021Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environmentcited by 2
  10. 2024Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehiclescited by 2
  11. 2024Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scalecited by 2
  12. 2024Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational testcited by 1
7 more
  1. 2024Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learningcited by 1
  2. 2023ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Eventscited by 0
  3. 2023Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersectionscited by 0
  4. 2024Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Datacited by 0
  5. 2024Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policiescited by 0
  6. 2024Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controlscited by 0
  7. 2025Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmarkcited by 0