8 citations · 22 across the 16 of their papers we have counts for
17 papers
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
Svar Rajankumar Patel, Kshitij Jerath
In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordination is b…
S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning
Kshitij Kumar Srivastava, Kshitij Jerath
Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution. It has been widely successful in solving long-horizon and complex tasks,…
Relational Weight Optimization for Enhancing Team Performance in Multi-Agent Multi-Armed Bandits
Monish Reddy Kotturu, Saniya Vahedian Movahed, Paul Robinette +3
We introduce an approach to improve team performance in a Multi-Agent Multi-Armed Bandit (MAMAB) framework using Fastest Mixing Markov Chain (FMMC) and Fastest Distributed Linear A…
Graph Attention Inference of Network Topology in Multi-Agent Systems
Akshay Kolli, Reza Azadeh, Kshitj Jerath
Accurately identifying the underlying graph structures of multi-agent systems remains a difficult challenge. Our work introduces a novel machine learning-based solution that levera…
Relational Q-Functionals: Multi-Agent Learning to Recover from Unforeseen Robot Malfunctions in Continuous Action Domains
Yasin Findik, Paul Robinette, Kshitij Jerath +1
Cooperative multi-agent learning methods are essential in developing effective cooperation strategies in multi-agent domains. In robotics, these methods extend beyond multi-robot s…
Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets
Zhaohui Yang, Kshitij Jerath
Recent endeavors aimed at forecasting future traffic flow states through deep learning encounter various challenges and yield diverse outcomes. A notable obstacle arises from the s…
Multi-scale Traffic Flow Modeling: A Renormalization Group Approach
Zhaohui Yang, Kshitij Jerath
Traffic flow modeling is typically performed at one of three different scales (microscopic, mesoscopic, or macroscopic), each with distinct modeling approaches. Recent works that a…
Iterative Forgetting: Online Data Stream Regression Using Database-Inspired Adaptive Granulation
Niket Kathiriya, Hossein Haeri, Cindy Chen +1
Many modern systems, such as financial, transportation, and telecommunications systems, are time-sensitive in the sense that they demand low-latency predictions for real-time decis…
Human-guided Swarms: Impedance Control-inspired Influence in Virtual Reality Environments
Spencer Barclay, Kshitij Jerath
Prior works in human-swarm interaction (HSI) have sought to guide swarm behavior towards established objectives, but may be unable to handle specific scenarios that require finer h…
Impact of Relational Networks in Multi-Agent Learning: A Value-Based Factorization View
Yasin Findik, Paul Robinette, Kshitij Jerath +1
Effective coordination and cooperation among agents are crucial for accomplishing individual or shared objectives in multi-agent systems. In many real-world multi-agent systems, ag…
Influence of Team Interactions on Multi-Robot Cooperation: A Relational Network Perspective
Yasin Findik, Hamid Osooli, Paul Robinette +2
Relational networks within a team play a critical role in the performance of many real-world multi-robot systems. To successfully accomplish tasks that require cooperation and coor…
Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams
Yasin Findik, Paul Robinette, Kshitij Jerath +1
Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared…
A Multi-Robot Task Assignment Framework for Search and Rescue with Heterogeneous Teams
Hamid Osooli, Paul Robinette, Kshitij Jerath +1
In post-disaster scenarios, efficient search and rescue operations involve collaborative efforts between robots and humans. Existing planning approaches focus on specific aspects b…
Thermodynamics-inspired Macroscopic States of Bounded Swarms
Hossein Haeri, Kshitij Jerath, Jacob Leachman
The collective behavior of swarms is extremely difficult to estimate or predict, even when the local agent rules are known and simple. The presented work seeks to leverage the simi…
DECISIVE Benchmarking Data Report: sUAS Performance Results from Phase I
Adam Norton, Reza Ahmadzadeh, Kshitij Jerath +18
This report reviews all results derived from performance benchmarking conducted during Phase I of the Development and Execution of Comprehensive and Integrated Subterranean Intelli…
DECISIVE Test Methods Handbook: Test Methods for Evaluating sUAS in Subterranean and Constrained Indoor Environments, Version 1.1
Adam Norton, Reza Ahmadzadeh, Kshitij Jerath +18
This handbook outlines all test methods developed under the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) projec…
Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior
Hossein Haeri, Reza Ahmadzadeh, Kshitij Jerath
In this work, we integrate `social' interactions into the MARL setup through a user-defined relational network and examine the effects of agent-agent relations on the rise of emerg…