2 papers
eess.SY2026
Enhancing Sample Efficiency in Multi-Agent RL with Uncertainty Quantification and Selective Exploration
Tom Danino, Nahum Shimkin
Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more…
cs.LG2024
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
Matthias Gerstgrasser, Tom Danino, Sarah Keren
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe…