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Found 7 papers, 4 papers with code
EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning
To address this, we introduce a reinforcement learning based RV that employs a congestion stage classifier to optimize the safety, efficiency, and stability of mixed traffic.
Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning
However, defining objectives of RL agents in traffic control and management tasks, as well as aligning policies with these goals through an effective formulation of Markov Decision Process (MDP), can be challenging and often require domain experts in both RL and ITS.
Efficient Quality-Diversity Optimization through Diverse Quality Species
This can be rectified by Quality-Diversity (QD) algorithms, where a population of high-quality and diverse solutions to a problem is preferred.
Mixed Traffic Control and Coordination from Pixels
In certain scenarios, our approach even outperforms using precision observations, e. g., up to 8% increase in average vehicle velocity in the merge environment, despite only using local traffic information as opposed to global traffic information.
AutoJoin: Efficient Adversarial Training for Robust Maneuvering via Denoising Autoencoder and Joint Learning
As a result of increasingly adopted machine learning algorithms and ubiquitous sensors, many 'perception-to-control' systems are developed and deployed.
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models
While the prediction accuracy of deep learning models is high, these models' robustness has raised many safety concerns, given that imperceptible perturbations added to input can substantially degrade the model performance.
Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning
Autonomous micromobility has been attracting the attention of researchers and practitioners in recent years.
