GALOPP: Multi-Agent Deep Reinforcement Learning For Persistent Monitoring With Localization Constraints. - Manav Mishra; Prithvi Poddar; Jingxi Chen; Pratap Tokekar; P. B. Sujit (CoRR, 2021)

A Model-free Deep Reinforcement Learning Approach To Maneuver A Quadrotor Despite Single Rotor Failure. - Paras Sharma; Prithvi Poddar; P. B. Sujit (CoRR, 2021)

Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policy Optimization. - Souradip Chakraborty; Amrit Singh Bedi; Kasun Weerakoon; Prithvi Poddar; Alec Koppel; Pratap Tokekar; Dinesh Manocha (ICRA, 2023)

RE-MOVE: An Adaptive Policy Design Approach for Dynamic Environments via Language-Based Feedback. - Souradip Chakraborty; Kasun Weerakoon; Prithvi Poddar; Pratap Tokekar; Amrit Singh Bedi; Dinesh Manocha (CoRR, 2023)

A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility. - Prithvi Poddar; Steve Paul; Souma Chowdhury (CoRR, 2024)

Multi-Agent Deep Reinforcement Learning for Persistent Monitoring With Sensing, Communication, and Localization Constraints. - Manav Mishra; Prithvi Poddar; Rajat Agrawal; Jingxi Chen; Pratap Tokekar; P. B. Sujit (IEEE Trans Autom. Sci. Eng., 2025)

Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning. - Prithvi Poddar; Ehsan Tarkesh Esfahani; Karthik Dantu; Souma Chowdhury (CoRR, 2025)