5 citations · 14 across the 12 of their papers we have counts for
13 papers
GCT-MARL: Graph-Based Contrastive Transfer for Sample-Efficient Cooperative Multi-Agent Reinforcement Learning
Animesh Animesh, Satheesh K Perepu, Kaushik Dey
In cooperative multi-agent reinforcement learning (MARL), from a deployment perspective, it is challenging and expensive to train agents from scratch for each new environment or ta…
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning
Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar +1
Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives. Prior work has investigated…
Know your Trajectory -- Trustworthy Reinforcement Learning deployment through Importance-Based Trajectory Analysis
Clifford F, Devika Jay, Abhishek Sarkar +4
As Reinforcement Learning (RL) agents are increasingly deployed in real-world applications, ensuring their behavior is transparent and trustworthy is paramount. A key component of…
Advanced Machine Learning Framework for Efficient Plant Disease Prediction
Aswath Muthuselvam, S. Sowdeshwar, M. Saravanan +1
Recently, Machine Learning (ML) methods are built-in as an important component in many smart agriculture platforms. In this paper, we explore the new combination of advanced ML met…
Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks
Kaushik Dey, Satheesh K. Perepu, Abir Das +1
Intent Management Function (IMF) is an integral part of future-generation networks. In recent years, there has been some work on AI-based IMFs that can handle conflicting intents a…
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs
Kaushik Dey, Satheesh K. Perepu, Abir Das
Intent-based management will play a critical role in achieving customers' expectations in the next-generation mobile networks. Traditional methods cannot perform efficient resource…