Design of an Active Vision System for High-Level Isolation Units through Q-Learning

Design of an Active Vision System for High-Level Isolation Units through Q-Learning

The inspection of Personal Protective Equipment (PPE) is one of the most necessary measures when treating patients affected by infectious diseases, such as Ebola or COVID-19. Assuring the integrity of health personnel in contact with infected patients has become an important concern in developed cou...

Full description

Bibliographic Details
Main Authors:	Andrea Gil Ruiz, Juan G. Victores, Bartek Łukawski, Carlos Balaguer
Format:	Article
Language:	English
Published:	MDPI AG 2020-08-01
Series:	Applied Sciences
Subjects:	reinforcement learning personal protective equipment Q-Learning reward shaping grid search healthcare
Online Access:	https://www.mdpi.com/2076-3417/10/17/5927

Similar Items

Reinforcement Q-Learning Control With Reward Shaping Function for Swing Phase Control in a Semi-active Prosthetic Knee
by: Yonatan Hutabarat, et al.
Published: (2020-11-01)

Reward Shaping Based Federated Reinforcement Learning
by: Yiqiu Hu, et al.
Published: (2021-01-01)

Calibration-Free Monocular Vision-Based Robot Manipulations With Occlusion Awareness
by: Yongle Luo, et al.
Published: (2021-01-01)

Subgoal-Based Reward Shaping to Improve Efficiency in Reinforcement Learning
by: Takato Okudo, et al.
Published: (2021-01-01)

An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users
by: Adam Bignold, et al.
Published: (2021-02-01)

MARLA-SG: Multi-Agent Reinforcement Learning Algorithm for Efficient Demand Response in Smart Grid
by: Sally Aladdin, et al.
Published: (2020-01-01)

Rewards Prediction-Based Credit Assignment for Reinforcement Learning With Sparse Binary Rewards
by: Minah Seo, et al.
Published: (2019-01-01)

Learning Reward Function with Matching Network for Mapless Navigation
by: Qichen Zhang, et al.
Published: (2020-06-01)

Maximize Producer Rewards in Distributed Windmill Environments: A Q-Learning Approach
by: Bei Li, et al.
Published: (2015-03-01)

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach
by: Sanjoy Kumar Mukherjee, et al.
Published: (2017-10-01)

Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System
by: Minhoe Kim, et al.
Published: (2020-11-01)

Multi-Agent Deep Q Network to Enhance the Reinforcement Learning for Delayed Reward System
by: Kim, K.
Published: (2022)

A Study on Machine Vision Techniques for the Inspection of Health Personnels’ Protective Suits for the Treatment of Patients in Extreme Isolation
by: Alice Stazio, et al.
Published: (2019-06-01)

A Multi-Dimensional Goal Aircraft Guidance Approach Based on Reinforcement Learning with a Reward Shaping Algorithm
by: Wenqiang Zu, et al.
Published: (2021-08-01)

Dynamic Spectrum Access with Deep Q-learning in Densely Occupied and Partially Observable Environments
by: S. Tomovic, et al.
Published: (2021-07-01)

Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis
by: Jorai Rijsdijk, et al.
Published: (2021-07-01)

Bat Q-learning Algorithm
by: Bilal Hashem Abed-alguni
Published: (2017-04-01)

Double Q-Learning for Radiation Source Detection
by: Zheng Liu, et al.
Published: (2019-02-01)

Parallel Implementation of Reinforcement Learning Q-Learning Technique for FPGA
by: Lucileide M. D. Da Silva, et al.
Published: (2019-01-01)

Constrained Deep Q-Learning Gradually Approaching Ordinary Q-Learning
by: Shota Ohnishi, et al.
Published: (2019-12-01)

Embodied Evolution of Learning Ability
by: Elfwing, Stefan
Published: (2007)

A Self-Adaptive Reinforcement-Exploration Q-Learning Algorithm
by: Lieping Zhang, et al.
Published: (2021-06-01)

Empirical explorations of strategic reinforcement learning: a case study in the sorting problem
by: Jung-Sing Jwo, et al.
Published: (2020-05-01)

Learning Strategies in Multi-Agent Systems - Applications to the Herding Problem
by: Gadre, Aditya Shrikant
Published: (2014)

Q-Learning Algorithms: A Comprehensive Classification and Applications
by: Beakcheol Jang, et al.
Published: (2019-01-01)

The Straw That Broke the Camel’s Back: Natural Variations in 17β-Estradiol and COMT-Val158Met Genotype Interact in the Modulation of Model-Free and Model-Based Control
by: Esther K. Diekhof, et al.
Published: (2021-07-01)

Cooperative Deep Q-Learning With Q-Value Transfer for Multi-Intersection Signal Control
by: Hongwei Ge, et al.
Published: (2019-01-01)

Research on fuzzing method of stateful network protocol based on Q-learning algorithm
by: Jing Chen, et al.
Published: (2020-04-01)

Q-Learning: Ett sätt att lära agenter att spela fotboll
by: Ekelund, Kalle
Published: (2013)

Non-Communication Decentralized Multi-Robot Collision Avoidance in Grid Map Workspace with Double Deep Q-Network
by: Lin Chen, et al.
Published: (2021-01-01)

QoE‐aware Q‐learning resource allocation for NOMA wireless multimedia communications
by: Shuan He, et al.
Published: (2020-09-01)

A line follower robot implementation using Lego's Mindstorms Kit and Q-Learning
by: Hector-Gabriel Acosta-Mesa, et al.
Published: (2012-03-01)

Cooperative Multi-Agent Reinforcement Learning with Conversation Knowledge for Dialogue Management
by: Shuyu Lei, et al.
Published: (2020-04-01)

Exploring Deep Recurrent Q-Learning for Navigation in a 3D Environment
by: Rasmus Kongsmar Brejl, et al.
Published: (2018-01-01)

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment
by: Fengjie Sun, et al.
Published: (2021-06-01)

Enhancements Of Fuzzy Q-Learning Algorithm
by: Grzegorz Głowaty
Published: (2005-01-01)

Robot control using Q-Learning
by: Szántó Zoltán
Published: (2012-06-01)

Value and prediction error in the medial frontal cortex: integrating the single-unit and systems levels of analysis
by: Massimo eSilvetti, et al.
Published: (2011-08-01)

Machine Learning for Traffic Control of Unmanned Mining Machines : Using the Q-learning and SARSA algorithms
by: Gustafsson, Robin, et al.
Published: (2019)

Train Scheduling with Deep Q-Network: A Feasibility Test
by: Intaek Gong, et al.
Published: (2020-11-01)