2 papers
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…
cs.NE2023
Evolutionary Strategy Guided Reinforcement Learning via MultiBuffer Communication
Adam Callaghan, Karl Mason, Patrick Mannion
Evolutionary Algorithms and Deep Reinforcement Learning have both successfully solved control problems across a variety of domains. Recently, algorithms have been proposed which co…