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cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
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…