most citedSelectively Sharing Experiences Improves Multi-Agent Reinforcement Learning

4 citations · 4 across the 2 of their papers we have counts for

collaborators

2 papers

eess.SY2025

Ensemble-MIX: Enhancing Sample-Efficiency in Multi-Agent RL via Selective Ensemble-Based 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.LG2023★ 4 cited

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…