8 papers
Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning
Davide Domini, Gianluca Aguzzi, Ivana Dusparic +2
Federated Learning often suffers under non-independently and identically distributed data, where a single global model may fail to represent the diversity of client distributions.…
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.…
Adapting the Behavior of Reinforcement Learning Agents to Changing Action Spaces and Reward Functions
Raul de la Rosa, Ivana Dusparic, Nicolas Cardozo
Reinforcement Learning (RL) agents often struggle in real-world applications where environmental conditions are non-stationary, particularly when reward functions shift or the avai…
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…
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning
Alberto Castagna
Reinforcement Learning (RL) enables an intelligent agent to optimise its performance in a task by continuously taking action from an observed state and receiving a feedback from th…