3 papers
cs.LG2026
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…
cs.AI2026
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…
cs.LG2025
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…