5 citations · 11 across the 9 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity
Kaushik Dey, Satheesh K. Perepu, Pallab Dasgupta +1
The dynamic and evolutionary nature of service requirements in wireless networks has motivated the telecom industry to consider intelligent self-adapting Reinforcement Learning (RL…
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning
Satheesh K. Perepu, Kaushik Dey
Multi-Agent reinforcement learning has received lot of attention in recent years and have applications in many different areas. Existing methods involving Centralized Training and…
Zero-Shot Federated Learning with New Classes for Audio Classification
Gautham Krishna Gudur, Satheesh K. Perepu
Federated learning is an effective way of extracting insights from different user devices while preserving the privacy of users. However, new classes with completely unseen data di…