Deep Reinforcement Learning Tf-agent-based Object Tracking with Virtual Autonomous Drone in a Game Engine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254663" target="_blank" >RIV/61989100:27240/23:10254663 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10286478" target="_blank" >https://ieeexplore.ieee.org/document/10286478</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3325062" target="_blank" >10.1109/ACCESS.2023.3325062</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Reinforcement Learning Tf-agent-based Object Tracking with Virtual Autonomous Drone in a Game Engine

Popis výsledku v původním jazyce

The recent development of object-tracking framework inventions has affected the performance of many manufacturing and service industries, such as product delivery, autonomous driving systems, security systems, military and transportation, retailing industries, smart cities, healthcare systems, agriculture, etc. Object tracking in physical environments and conditions is much more challenging to achieve accurate results. However, the process can be experimented using simulation techniques or platforms to evaluate and check the model&apos;s performance under different simulation conditions and weather changes. This paper represents one of the target tracking approaches based on the reinforcement learning technique integrated with tf-agent (TensorFlow-Agent) to accomplish the tracking process in the Unreal Game Engine simulation platform, Blocks. The productivity of these platforms can be seen while experimenting in virtual-reality conditions with virtual drone agents and performing fine-tuning to achieve the best or desired performance. In this proposal, the tf-agent drone learns how to track an object integration with a deep reinforcement learning process to control the actions, states, and tracking by receiving sequential frames from a simple Blocks environment. The TF-agent is trained in a Blocks environment for adaptation to the environment and existing objects in a simulation environment for further testing and evaluation regarding the accuracy of tracking and speed. We have tested and compared two approaches to the algorithm methods based on the DQN and PPO trackers integrated with the simulation process regarding stability, rewards, and numerical performance. Author

Název v anglickém jazyce

Deep Reinforcement Learning Tf-agent-based Object Tracking with Virtual Autonomous Drone in a Game Engine

Popis výsledku anglicky

The recent development of object-tracking framework inventions has affected the performance of many manufacturing and service industries, such as product delivery, autonomous driving systems, security systems, military and transportation, retailing industries, smart cities, healthcare systems, agriculture, etc. Object tracking in physical environments and conditions is much more challenging to achieve accurate results. However, the process can be experimented using simulation techniques or platforms to evaluate and check the model&apos;s performance under different simulation conditions and weather changes. This paper represents one of the target tracking approaches based on the reinforcement learning technique integrated with tf-agent (TensorFlow-Agent) to accomplish the tracking process in the Unreal Game Engine simulation platform, Blocks. The productivity of these platforms can be seen while experimenting in virtual-reality conditions with virtual drone agents and performing fine-tuning to achieve the best or desired performance. In this proposal, the tf-agent drone learns how to track an object integration with a deep reinforcement learning process to control the actions, states, and tracking by receiving sequential frames from a simple Blocks environment. The TF-agent is trained in a Blocks environment for adaptation to the environment and existing objects in a simulation environment for further testing and evaluation regarding the accuracy of tracking and speed. We have tested and compared two approaches to the algorithm methods based on the DQN and PPO trackers integrated with the simulation process regarding stability, rewards, and numerical performance. Author

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

11

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

124129-124138

Kód UT WoS článku

001104556800001

EID výsledku v databázi Scopus

2-s2.0-85174831696