63 citations · 77 across the 7 of their papers we have counts for
4 papers · 1 filter
AccMER: Accelerating Multi-Agent Experience Replay with Cache Locality-aware Prioritization
Kailash Gogineni, Yongsheng Mei, Peng Wei +2
Multi-Agent Experience Replay (MER) is a key component of off-policy reinforcement learning~(RL) algorithms. By remembering and reusing experiences from the past, experience replay…
Towards Efficient Multi-Agent Learning Systems
Kailash Gogineni, Peng Wei, Tian Lan +1
Multi-Agent Reinforcement Learning (MARL) is an increasingly important research field that can model and control multiple large-scale autonomous systems. Despite its achievements,…
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
Yongsheng Mei, Hanhan Zhou, Tian Lan +2
Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay signific…
Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems
Kailash Gogineni, Peng Wei, Tian Lan +1
Multi-Agent Reinforcement Learning (MARL) is a promising area of research that can model and control multiple, autonomous decision-making agents. During online training, MARL algor…