10 citations · 30 across the 9 of their papers we have counts for
Showing cs.MAShow all
2 papers · 1 filter
cs.MA2023★ 2 cited
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
Lukas Schäfer, Oliver Slumbers, Stephen McAleer +3
Multi-agent reinforcement learning (MARL) requires agents to explore within a vast joint action space to find joint actions that lead to coordination. Existing value-based MARL alg…
cs.MA2021★ 3 cited
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
David Henry Mguni, Taher Jafferjee, Jianhong Wang +7
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to joint…