Search Results for author: Ivana Dusparic

Found 25 papers, 5 papers with code

ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies

no code implementations9 Feb 2024 Jasmina Gajcin, Ivana Dusparic

In this work, we propose ACTER (Actionable Counterfactual Sequences for Explaining Reinforcement Learning Outcomes), an algorithm for generating counterfactual sequences that provides actionable advice on how failure can be avoided.

counterfactual Counterfactual Reasoning +2

Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification

1 code implementation30 Aug 2023 Jasmina Gajcin, James McCarthy, Rahul Nair, Radu Marinescu, Elizabeth Daly, Ivana Dusparic

Our approach allows the user to provide trajectory-level feedback on agent's behavior during training, which can be integrated as a reward shaping signal in the following training iteration.

Reinforcement Learning (RL)

Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks

no code implementations14 Jun 2023 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.

Multi-agent Reinforcement Learning reinforcement-learning

Reservation of Virtualized Resources with Optimistic Online Learning

no code implementations15 Mar 2023 Jean-Baptiste Monteil, George Iosifidis, Ivana Dusparic

The different service providers (SPs) have the opportunity to lease the network resources from the NO to constitute slices that address the demand of their specific network service.

Decision Making

RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning

1 code implementation8 Mar 2023 Jasmina Gajcin, Ivana Dusparic

In this work, we propose RACCER, the first RL-specific approach to generating counterfactual explanations for the behavior of RL agents.

counterfactual reinforcement-learning +1

Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning

no code implementations2 Mar 2023 Alberto Castagna, Ivana Dusparic

As an alternative, in this paper we propose Expert-Free Online Transfer Learning (EF-OnTL), an algorithm that enables expert-free real-time dynamic transfer learning in multi-agent system.

Multi-agent Reinforcement Learning reinforcement-learning +2

Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning

1 code implementation9 Nov 2022 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 state spaces.

reinforcement-learning Reinforcement Learning (RL)

Causal Counterfactuals for Improving the Robustness of Reinforcement Learning

2 code implementations2 Nov 2022 Tom He, Jasmina Gajcin, Ivana Dusparic

We apply CausalCF to complex robotic tasks and show that it improves the RL agent's robustness using CausalWorld.

Causal Inference reinforcement-learning +2

Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities

no code implementations21 Oct 2022 Jasmina Gajcin, Ivana Dusparic

Additionally, we explore the differences between counterfactual explanations in supervised learning and RL and identify the main challenges that prevent the adoption of methods from supervised in reinforcement learning.

counterfactual reinforcement-learning +1

Boolean Decision Rules for Reinforcement Learning Policy Summarisation

no code implementations18 Jul 2022 James McCarthy, Rahul Nair, Elizabeth Daly, Radu Marinescu, Ivana Dusparic

Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context.

reinforcement-learning Reinforcement Learning (RL)

FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing

no code implementations23 May 2022 Helio N. Cunha Neto, Ivana Dusparic, Diogo M. F. Mattos, Natalia C. Fernandes

This paper proposes the Federated Simulated Annealing (FedSA) metaheuristic to select the hyperparameters and a subset of participants for each aggregation round in federated learning.

Federated Learning Intrusion Detection

Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning

no code implementations4 Apr 2022 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.

Multi-agent Reinforcement Learning reinforcement-learning +1

ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning

1 code implementation21 Mar 2022 Jasmina Gajcin, Ivana Dusparic

We propose ReCCoVER, an algorithm which detects causal confusion in agent's reasoning before deployment, by executing its policy in alternative environments where certain correlations between features do not hold.

feature selection reinforcement-learning +1

Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents

no code implementations17 Dec 2021 Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati, Radu Marinescu, Elizabeth Daly, Ivana Dusparic

In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, but employ different strategies can be trained by adjusting the impact of individual objectives on reward function.

Autonomous Driving reinforcement-learning +1

Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems

no code implementations1 Dec 2021 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.

reinforcement-learning Reinforcement Learning (RL) +1

Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points

no code implementations3 Nov 2021 Boris Galkin, Babatunji Omoniwa, Ivana Dusparic

In this paper, we propose a multi-agent deep reinforcement learning approach to optimise the energy efficiency of fixed-wing UAV cellular access points while still allowing them to deliver high-quality service to users on the ground.

Trajectory Planning

Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning

no code implementations1 Jun 2021 Babatunji Omoniwa, Boris Galkin, Ivana Dusparic

Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be deployed to provide wireless connectivity to ground devices in events of increased network demand, points-of-failure in existing infrastructure, or disasters.

Decision Making Q-Learning

A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT

no code implementations1 Jun 2021 Babatunji Omoniwa, Maxime Gueriau, Ivana Dusparic

As such, power-control mechanisms and intelligent mobility of the relay devices are critical in improving communication performance and energy utilization.

Industrial Robots Q-Learning

Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options

no code implementations11 Mar 2021 Nicolás Cardozo, Ivana Dusparic

Options are explored in interaction with the environment, and the most suitable options for each context are used to generate adaptations exploiting COP.

reinforcement-learning Reinforcement Learning (RL)

Adaptation to Unknown Situations as the Holy Grail of Learning-Based Self-Adaptive Systems: Research Directions

no code implementations11 Mar 2021 Ivana Dusparic, Nicolas Cardozo

Capturing all possible changes to define suitable behaviour beforehand is unfeasible, or even impossible in the case of unknown changes, hence human intervention may be required.

Mobility for Cellular-Connected UAVs: challenges for the network provider

no code implementations25 Feb 2021 Erika Fonseca, Boris Galkin, Marvin Kelly, Luiz A. DaSilva, Ivana Dusparic

Unmanned Aerial Vehicle (UAV) technology is becoming more prevalent and more diverse in its application.

Networking and Internet Architecture

Experimental Evaluation of a UAV User QoS from a Two-Tier 3.6GHz Spectrum Network

no code implementations6 Nov 2020 Boris Galkin, Erika Fonseca, Gavin Lee, Conor Duff, Marvin Kelly, Edward Emmanuel, Ivana Dusparic

Our results show that increasing the UAV height reduces the performance in both tiers, due to issues such as antenna misalignment.

Networking and Internet Architecture

Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning

no code implementations27 Jul 2020 Erika Fonseca, Boris Galkin, Ramy Amer, Luiz A. DaSilva, Ivana Dusparic

On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.

reinforcement-learning Reinforcement Learning (RL)

Decentralised Multi-Agent Reinforcement Learning for Dynamic and Uncertain Environments

no code implementations16 Sep 2014 Andrei Marinescu, Ivana Dusparic, Adam Taylor, Vinny Cahill, Siobhán Clarke

In this paper we propose P-MARL, a decentralised MARL algorithm enhanced by a prediction mechanism that provides accurate information regarding up-coming changes in the environment.

Multiagent Systems

Cannot find the paper you are looking for? You can Submit a new open access paper.