33 citations · 69 across the 13 of their papers we have counts for
13 papers
Emergent Behaviors in Multi-Agent Target Acquisition
Piyush K. Sharma, Erin Zaroukian, Derrik E. Asher +1
Only limited studies and superficial evaluations are available on agents' behaviors and roles within a Multi-Agent System (MAS). We simulate a MAS using Reinforcement Learning (RL)…
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination
Derrik E. Asher, Anjon Basak, Rolando Fernandez +9
Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provid…
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
Gangshan Jing, He Bai, Jemin George +2
Achieving distributed reinforcement learning (RL) for large-scale cooperative multi-agent systems (MASs) is challenging because: (i) each agent has access to only limited informati…
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph
Gangshan Jing, He Bai, Jemin George +2
Existing distributed cooperative multi-agent reinforcement learning (MARL) frameworks usually assume undirected coordination graphs and communication graphs while estimating a glob…
COSMic: A Coherence-Aware Generation Metric for Image Descriptions
Mert İnan, Piyush Sharma, Baber Khalid +3
Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled…
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training
Piyush K. Sharma, Rolando Fernandez, Erin Zaroukian +3
Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) appro…
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
Gangshan Jing, He Bai, Jemin George +2
Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL). Unfortunately, in the gradient estim…
Heterogeneous Noisy Short Signal Camouflage in Multi-Domain Environment Decision-Making
Piyush K. Sharma
Data transmission between two or more digital devices in industry and government demands secure and agile technology. Digital information distribution often requires deployment of…
IoT Solutions with Multi-Sensor Fusion and Signal-Image Encoding for Secure Data Transfer and Decision Making
Piyush K. Sharma, Mark Dennison, Adrienne Raglin
Deployment of Internet of Things (IoT) devices and Data Fusion techniques have gained popularity in public and government domains. This usually requires capturing and consolidating…
Investigating Manifold Neighborhood size for Nonlinear Analysis of LIBS Amino Acid Spectra
Piyush K. Sharma, Gary Holness, Poopalasingam Sivakumar +2
Classification and identification of amino acids in aqueous solutions is important in the study of biomacromolecules. Laser Induced Breakdown Spectroscopy (LIBS) uses high energy l…
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding +1
The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. Howev…
Learning Distributed Stabilizing Controllers for Multi-Agent Systems
Gangshan Jing, He Bai, Jemin George +2
We address the problem of model-free distributed stabilization of heterogeneous multi-agent systems using reinforcement learning (RL). Two algorithms are developed. The first algor…
Understanding Guided Image Captioning Performance across Domains
Edwin G. Ng, Bo Pang, Piyush Sharma +1
Image captioning models generally lack the capability to take into account user interest, and usually default to global descriptions that try to balance readability, informativenes…