10 citations · 19 across the 19 of their papers we have counts for
19 papers
Diagnosis of Human Object Interaction Detectors for Real World Educational Applications
Divya Mereddy, Ashwin Tudur Sadashiva, Marcos Quinones-Grueiro +1
Human-object interaction (HOI) recognition is critical for automatically analyzing student behavior in complex educational environments. Although state-of-the-art (SOTA) HOI detect…
Enabling Real-Time Phase Control in Traffic Signal Hardware-in-the-Loop Simulation
Zhiyao Zhang, Gergely Zachár, William Barbour +4
Advanced Traffic Signal Control (TSC) algorithms require real-time phase control, yet existing Hardware-in-the-Loop Simulation (HILS) testbeds only support pre-programmed timing pl…
Safe Continual Reinforcement Learning in Non-stationary Environments
Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro +1
Reinforcement learning (RL) offers a compelling data-driven paradigm for synthesizing controllers for complex systems when accurate physical models are unavailable; however, most e…
Video-Based Performance Evaluation for ECR Drills in Synthetic Training Environments
Surya Rayala, Marcos Quinones-Grueiro, Naveeduddin Mohammed +5
Effective urban warfare training requires situational awareness and muscle memory, developed through repeated practice in realistic yet controlled environments. A key drill, Enter…
Trainee Action Recognition through Interaction Analysis in CCATT Mixed-Reality Training
Divya Mereddy, Marcos Quinones-Grueiro, Ashwin T S +7
This study examines how Critical Care Air Transport Team (CCATT) members are trained using mixed-reality simulations that replicate the high-pressure conditions of aeromedical evac…
Real-World Deployment and Assessment of a Multi-Agent Reinforcement Learning-Based Variable Speed Limit Control System
Yuhang Zhang, Zhiyao Zhang, Junyi Ji +7
This article presents the first field deployment of a multi-agent reinforcement learning (MARL) based variable speed limit (VSL) control system on Interstate 24 (I-24) near Nashvil…
On the Design of Safe Continual RL Methods for Control of Nonlinear Systems
Austin Coursey, Marcos Quinones-Grueiro, Gautam Biswas
Reinforcement learning (RL) algorithms have been successfully applied to control tasks associated with unmanned aerial vehicles and robotics. In recent years, safe RL has been prop…
Improving Computational Cost of Bayesian Optimization for Controller Tuning with a Multi-stage Tuning Framework
Marlon J. Ares-Milian, Gregory Provan, Marcos Quinones-Grueiro
Control auto-tuning for industrial and robotic systems, when framed as an optimization problem, provides an excellent means to tune these systems. However, most optimization method…
Phase Re-service in Reinforcement Learning Traffic Signal Control
Zhiyao Zhang, George Gunter, Marcos Quinones-Grueiro +4
This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of t…
Field Deployment of Multi-Agent Reinforcement Learning Based Variable Speed Limit Controllers
Yuhang Zhang, Zhiyao Zhang, Marcos Quiñones-Grueiro +4
This article presents the first field deployment of a multi-agent reinforcement-learning (MARL) based variable speed limit (VSL) control system on the I-24 freeway near Nashville,…
FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection
Austin Coursey, Junyi Ji, Marcos Quinones-Grueiro +5
Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and errors in event ident…
MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits
Yuhang Zhang, Marcos Quinones-Grueiro, Zhiyao Zhang +4
Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting spee…
A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances
Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
In this work, we present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs). UAVs are dynamic systems where control decisions in response…
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems
Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
In this paper, we leverage ideas from model-based control to address the sample efficiency problem of reinforcement learning (RL) algorithms. Accelerating learning is an active fie…
Concurrent Policy Blending and System Identification for Generalized Assistive Control
Luke Bhan, Marcos Quinones-Grueiro, Gautam Biswas
In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters. Our approach combines simultaneous policy blending with…
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning
Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
This paper discusses an Enhanced Model-Agnostic Meta-Learning (E-MAML) algorithm that generates fast convergence of the policy function from a small number of training examples whe…
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control
Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
Faults are endemic to all systems. Adaptive fault-tolerant control maintains degraded performance when faults occur as opposed to unsafe conditions or catastrophic events. In syste…
Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning
Ibrahim Ahmed, Marcos Quiñones-Grueiro, Gautam Biswas
We propose a novel adaptive reinforcement learning control approach for fault tolerant control of degrading systems that is not preceded by a fault detection and diagnosis step. Th…
A Relearning Approach to Reinforcement Learning for Control of Smart Buildings
Avisek Naug, Marcos Quiñones-Grueiro, Gautam Biswas
This paper demonstrates that continual relearning of control policies using incremental deep reinforcement learning (RL) can improve policy learning for non-stationary processes. W…