12 citations · 44 across the 41 of their papers we have counts for
10 papers · 1 filter
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…
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities
Jasmina Gajcin, Ivana Dusparic
While AI algorithms have shown remarkable success in various fields, their lack of transparency hinders their application to real-life tasks. Although explanations targeted at non-…
Communication-Enabled Deep Reinforcement Learning to Optimise Energy-Efficiency in UAV-Assisted Networks
Babatunji Omoniwa, Boris Galkin, Ivana Dusparic
Unmanned aerial vehicles (UAVs) are increasingly deployed to provide wireless connectivity to static and mobile ground users in situations of increased network demand or points of…
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…
Guidelines for Artifacts to Support Industry-Relevant Research on Self-Adaptation
Danny Weyns, Ilias Gerostathopoulos, Barbora Buhnova +16
Artifacts support evaluating new research results and help comparing them with the state of the art in a field of interest. Over the past years, several artifacts have been introdu…
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…
Enabling Deep Reinforcement Learning on Energy Constrained Devices at the Edge of the Network
Jernej Hribar, Ivana Dusparic
Deep Reinforcement Learning (DRL) solutions are becoming pervasive at the edge of the network as they enable autonomous decision-making in a dynamic environment. However, to be abl…