5 papers
Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback
Chenyang Zhao, Vinny Cahill, Ivana Dusparic
Reward design has been one of the central challenges for real world reinforcement learning (RL) deployment, especially in settings with multiple objectives. Preference-based RL off…
Geographically-aware Transformer-based Traffic Forecasting for Urban Motorway Digital Twins
KreÅ¡imir KuÅ¡iÄ, Vinny Cahill, Ivana Dusparic
The operational effectiveness of digital-twin technology in motorway traffic management depends on the availability of a continuous flow of high-resolution real-time traffic data.…
Continual Reinforcement Learning for Cyber-Physical Systems: Lessons Learned and Open Challenges
Kim N. Nolle, Ivana Dusparic, Rhodri Cusack +1
Continual learning (CL) is a branch of machine learning that aims to enable agents to adapt and generalise previously learned abilities so that these can be reapplied to new tasks…
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
Wenlong Wang, Ivana Dusparic, Yucheng Shi +2
Model-based reinforcement learning (RL) offers a solution to the data inefficiency that plagues most model-free RL algorithms. However, learning a robust world model often requires…
Applying Neural Monte Carlo Tree Search to Unsignalized Multi-intersection Scheduling for Autonomous Vehicles
Yucheng Shi, Wenlong Wang, Xiaowen Tao +2
Dynamic scheduling of access to shared resources by autonomous systems is a challenging problem, characterized as being NP-hard. The complexity of this task leads to a combinatoria…