2 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
Demonstration-Guided Continual Reinforcement Learning in Dynamic Environments
Xue Yang, Michael Schukat, Junlin Lu +3
Reinforcement learning (RL) excels in various applications but struggles in dynamic environments where the underlying Markov decision process evolves. Continual reinforcement learn…
MOMA-AC: A preference-driven actor-critic framework for continuous multi-objective multi-agent reinforcement learning
Adam Callaghan, Karl Mason, Patrick Mannion
This paper addresses a critical gap in Multi-Objective Multi-Agent Reinforcement Learning (MOMARL) by introducing the first dedicated inner-loop actor-critic framework for continuo…
Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance
Bram Silue, Santiago Amaya-Corredor, Patrick Mannion +2
Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by inferring dense reward functions from…
Ireland in 2057: Projections using a Geographically Diverse Dynamic Microsimulation
Seán Caulfield Curley, Karl Mason, Patrick Mannion
This paper presents a dynamic microsimulation model developed for Ireland, designed to simulate key demographic processes and individual life-course transitions from 2022 to 2057.…