4 citations · 10 across the 6 of their papers we have counts for
6 papers
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems
Kailash Gogineni, Sai Santosh Dayapule, Juan Gómez-Luna +6
Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets. However, RL training often faces memory limitations, lead…
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,…
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…
A Framework for Server Authentication using Communication Protocol Dialects
Kailash Gogineni, Yongsheng Mei, Guru Venkataramani +1
In today's world, computer networks have become vulnerable to numerous attacks. In both wireless and wired networks, one of the most common attacks is man-in-the-middle attacks, wi…
MPD: Moving Target Defense through Communication Protocol Dialects
Yongsheng Mei, Kailash Gogineni, Tian Lan +1
Communication protocol security is among the most significant challenges of the Internet of Things (IoT) due to the wide variety of hardware and software technologies involved. Mov…