1 citations · 2 across the 13 of their papers we have counts for
16 papers
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems
Juan C. Rosero, Ivana Dusparic, Nicolás Cardozo
Reinforcement Learning (RL) is used extensively in Autonomous Systems (AS) as it enables learning at runtime without the need for a model of the environment or predefined actions.…
Semifactual Explanations for Reinforcement Learning
Jasmina Gajcin, Jovan Jeromela, Ivana Dusparic
Reinforcement Learning (RL) is a learning paradigm in which the agent learns from its environment through trial and error. Deep reinforcement learning (DRL) algorithms represent th…
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks
Babatunji Omoniwa, Boris Galkin, Ivana Dusparic
Unmanned aerial vehicles (UAVs) serving as aerial base stations can be deployed to provide wireless connectivity to mobile users, such as vehicles. However, the density of vehicles…
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing
Helio N. Cunha Neto, Ivana Dusparic, Diogo M. F. Mattos +1
Fast identification of new network attack patterns is crucial for improving network security. Nevertheless, identifying an ongoing attack in a heterogeneous network is a non-trivia…
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning
Babatunji Omoniwa, Boris Galkin, Ivana Dusparic
In this letter, we study the energy efficiency (EE) optimisation of unmanned aerial vehicles (UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agen…
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…