3 citations · 3 across the 5 of their papers we have counts for
6 papers
CORE-RL: Confidence-Oriented Reliability Evaluation of Black-Box Reinforcement Learning Policies
Santhosh GS, Ananya Ravi, Devika Jay +5
The deployment of Reinforcement Learning (RL) agents in critical domains must be preceded with a pipeline to evaluate the alignment of the RL agent with complex multi-objective spe…
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…
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…
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…
Multi-agent reinforcement learning for intent-based service assurance in cellular networks
Satheesh K. Perepu, Jean P. Martins, Ricardo Souza S +1
Recently, intent-based management has received good attention in telecom networks owing to stringent performance requirements for many of the use cases. Several approaches in the l…