activity
20202025
most citedMultiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand

10 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2025

Real-time Remote Tracking and Autonomous Planning for Whale Rendezvous using Robots

Sushmita Bhattacharya, Ninad Jadhav, Hammad Izhar +4

We introduce a system for real-time sperm whale rendezvous at sea using an autonomous uncrewed aerial vehicle. Our system employs model-based reinforcement learning that combines i…

cs.MA2023★ 1 cited

Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)

Daniel Garces, Sushmita Bhattacharya, Dimitri Bertsekas +1

In this paper, we focus on the autonomous multiagent taxi routing problem for a large urban environment where the location and number of future ride requests are unknown a-priori,…

cs.MA2022★ 10 cited

Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand

Daniel Garces, Sushmita Bhattacharya, Stephanie Gil +1

We derive a learning framework to generate routing/pickup policies for a fleet of autonomous vehicles tasked with servicing stochastically appearing requests on a city map. We focu…

cs.RO2020★ 4 cited

Multiagent Rollout and Policy Iteration for POMDP with Application to Multi-Robot Repair Problems

Sushmita Bhattacharya, Siva Kailas, Sahil Badyal +2

In this paper we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, partial state observations, and a multiagent structure. We…

cs.RO2020

Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems

Sushmita Bhattacharya, Sahil Badyal, Thomas Wheeler +2

In this paper we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, and partial state observations. We discuss an algorithm tha…