mlsanityData to 7 Oct 2026

Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models

arXiv 2501.02548 ↗First posted 5 Jan 2025cs.LG cs.AIcited by 0

On the map: World models → World models for autonomous driving

Abstract (arXiv)

Deploying learned decision-making systems often requires transferring to new sites where the sensing pipeline differs. In such cases, observations can change in semantics and dimensionality even when action primitives and objectives remain comparable. In this work, we study transferable model-based planning under this observation mismatch, which remains challenging for existing learning-based approaches. We propose Adaptive Modularized Model (AMM), a modular planning architecture that separates a domain-specific observation adapter from a shared internal dynamics model defined in a common planning state space. The dynamics model is meta-learned from multiple source domains to enable fast adaptation with limited target interaction. At run time, AMM performs receding-horizon planning by rolling out candidate action sequences under the learned dynamics and selecting actions that optimize a task-specific objective over predicted futures. We instantiate the approach on cross-domain traffic signal control, where actions correspond to signal phases and the planning objective captures congestion. Experiments show that AMM improves both performance and data efficiency compared with existing conventional controllers and learning-based baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in World models for autonomous driving, one of 11 directions in World models. Builds on 18 of its 27 references that are in the map. First posted on arXiv 5 Jan 2025.

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18 of 27 references.

References · 18

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
  4. 2018World Modelscited by 1,444
  5. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  6. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  7. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,221
  8. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 748
  9. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  10. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 114
  11. 2019Learning Phase Competition for Traffic Signal Controlcited by 84
  12. 2019A Survey on Traffic Signal Control Methodscited by 78
6 more
  1. 2020AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Controlcited by 60
  2. 2021Domain Adaptation In Reinforcement Learning Via Latent Unified State Representationcited by 33
  3. 2021Efficient Pressure: Improving efficiency for signalized intersectionscited by 24
  4. 2022Transfer RL across Observation Feature Spaces via Model-Based Regularizationcited by 18
  5. 2023Reinforcement Learning Approaches for Traffic Signal Control under Missing Datacited by 13
  6. 2022Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Methodcited by 10

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  1. 2026LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control
  2. 2025Evolutionary Discovery of Heuristic Policies for Traffic Signal Control
  3. 2025LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
  4. 2026VLALight: A Vision-Language-Action Model for Traffic Signal Control
  5. 2026Spatiotemporal Decision Transformer for Traffic Coordination
  6. 2025Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
  7. 2025Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
  8. 2022Offline Reinforcement Learning for Road Traffic Control