12 citations · 44 across the 41 of their papers we have counts for
12 papers · 1 filter
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.…
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, Ivana Dusparic
Transfer learning in Reinforcement Learning (RL) has been widely studied to overcome training issues of Deep-RL, i.e., exploration cost, data availability and convergence time, by…
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
Tom He, Jasmina Gajcin, Ivana Dusparic
Reinforcement learning (RL) is used in various robotic applications. RL enables agents to learn tasks autonomously by interacting with the environment. The more critical the tasks…
Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning
Jernej Hribar, Luke Hackett, Ivana Dusparic
In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multi-objective tabular Reinforcement Learning (RL) algorithm W-learning to large…
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
James McCarthy, Rahul Nair, Elizabeth Daly +2
Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context. Understanding the decisions and…
ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning
Jasmina Gajcin, Ivana Dusparic
Despite notable results in various fields over the recent years, deep reinforcement learning (DRL) algorithms lack transparency, affecting user trust and hindering their deployment…
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems
Alberto Castagna, Ivana Dusparic
Reinforcement learning (RL) has been used in a range of simulated real-world tasks, e.g., sensor coordination, traffic light control, and on-demand mobility services. However, real…
A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT
Babatunji Omoniwa, Maxime Gueriau, Ivana Dusparic
Recent research has shown the potential of using available mobile fog devices (such as smartphones, drones, domestic and industrial robots) as relays to minimize communication outa…
Multi-agent Deep Reinforcement Learning for Zero Energy Communities
Amit Prasad, Ivana Dusparic
Advances in renewable energy generation and introduction of the government targets to improve energy efficiency gave rise to a concept of a Zero Energy Building (ZEB). A ZEB is a b…