12 citations · 19 across the 9 of their papers we have counts for
9 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…
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
Jasmina Gajcin, Ivana Dusparic
Understanding how failure occurs and how it can be prevented in reinforcement learning (RL) is necessary to enable debugging, maintain user trust, and develop personalized policies…
Learning Recovery Strategies for Dynamic Self-healing in Reactive Systems
Mateo Sanabria, Ivana Dusparic, Nicolas Cardozo
Self-healing systems depend on following a set of predefined instructions to recover from a known failure state. Failure states are generally detected based on domain specific spec…
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
Jasmina Gajcin, James McCarthy, Rahul Nair +3
A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging…
Reservation of Virtualized Resources with Optimistic Online Learning
Jean-Baptiste Monteil, George Iosifidis, Ivana Dusparic
The virtualization of wireless networks enables new services to access network resources made available by the Network Operator (NO) through a Network Slicing market. The different…