Aritra Mitra

This study examines how delays affect stochastic approximation in reinforcement learning.

This research simplifies the proof of convergence for TD learning with linear function approximation.

Examining how agents learn faster together despite communication delays.

A new approach for agents to share strategies and improve performance in diverse settings.

Improving Q-learning performance amid corrupted feedback in real-world applications.