4 papers
Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning
Kalyan Cherukuri, Aarav Lala
As generative agents become increasingly capable, alignment of their behavior with complex human values remains a fundamental challenge. Existing approaches often simplify human in…
Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
Kalyan Cherukuri, Aarav Lala, Yash Yardi
We propose Q-Policy, a hybrid quantum-classical reinforcement learning (RL) framework that mathematically accelerates policy evaluation and optimization by exploiting quantum compu…
Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
Aarav Lala, Kalyan Cherukuri
As artificial intelligence continues to drive innovation in complex, decentralized environments, the need for scalable, adaptive, and privacy-preserving decision-making systems has…
Low-Rank Matrix Approximation for Neural Network Compression
Kalyan Cherukuri, Aarav Lala
Deep Neural Networks (DNNs) have encountered an emerging deployment challenge due to large and expensive memory and computation requirements. In this paper, we present a new Adapti…