Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment

arXiv:2511.06229 · cs.LG · Submitted 2025-11-09 · Read on arXiv

cs.LG

Submitted: 2025-11-09

Updated: 2026-08-30

Code: https://github.com/dgmin-kr/dode-rl-sumo

Terminology

• Calibration — 33× in this paper · explained in 7 episodes
• Credit assignment — 21× in this paper · explained in 3 episodes
• Reinforcement Learning — 15× in this paper · explained in 4 episodes
• Deep Reinforcement Learning — 10× in this paper · explained in 2 episodes
• Actor — 8× in this paper · explained in 3 episodes
• Reward Function — 7× in this paper · explained in 2 episodes
• Action Space — 6× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Bayesian Optimization — 5× in this paper · explained in 3 episodes
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Deep Reinforcement Learning (DRL) — 3× in this paper · explained in 2 episodes
• Markov Decision Process — 3× in this paper
• Markov Decision Process (MDP) — 3× in this paper · explained in 11 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Actor-Critic Architecture — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Bayesian Optimization (BO) — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• alpha — 2× in this paper · explained in 1 episode
• Acquisition Function — 1× in this paper · explained in 1 episode
• Actor-Critic Framework — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Digital Twin — 1× in this paper · explained in 4 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks