activity
20172022
most citedRisk-aware Resource Allocation for Multiple UAVs-UGVs Recharging Rendezvous

4 citations · 19 across the 16 of their papers we have counts for

collaborators

29 papers

cs.LG2022

Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information

Vishnu Dutt Sharma, John P. Dickerson, Pratap Tokekar

Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation. Prior works on GSG-I have used deep…

cs.RO2022

Approximation Algorithms for Robot Tours in Random Fields with Guaranteed Estimation Accuracy

Shamak Dutta, Nils Wilde, Pratap Tokekar +1

We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the…

cs.RO2022

D2CoPlan: A Differentiable Decentralized Planner for Multi-Robot Coverage

Vishnu Dutt Sharma, Lifeng Zhou, Pratap Tokekar

Centralized approaches for multi-robot coverage planning problems suffer from the lack of scalability. Learning-based distributed algorithms provide a scalable avenue in addition t…

cs.RO20224 cited

Risk-aware Resource Allocation for Multiple UAVs-UGVs Recharging Rendezvous

Ahmad Bilal Asghar, Guangyao Shi, Nare Karapetyan +4

We study a resource allocation problem for the cooperative aerial-ground vehicle routing application, in which multiple Unmanned Aerial Vehicles (UAVs) with limited battery capacit…

cs.RO2022

Risk-aware UAV-UGV Rendezvous with Chance-Constrained Markov Decision Process

Guangyao Shi, Nare Karapetyan, Ahmad Bilal Asghar +4

We study a chance-constrained variant of the cooperative aerial-ground vehicle routing problem, in which an Unmanned Aerial Vehicle (UAV) with limited battery capacity and an Unman…

cs.LG20221 cited

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces

Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil +3

We focus on parameterized policy search for reinforcement learning over continuous action spaces. Typically, one assumes the score function associated with a policy is bounded, whi…