RER: Recycled Experience Replay with Dual Memory Architecture for Path Planning of a Moving Target using Deep Reinforcement Learning

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dc.contributor.advisorShahnewaz Siddique
dc.contributor.authorSamiya Kabir Youme
dc.contributor.authorHossain Ahamed
dc.contributor.authorTowsif Alam Chowdhury
dc.contributor.authorSayeed Abid
dc.contributor.authorRusafa Binte Sohrawardi
dc.contributor.id1711848042
dc.contributor.id1711678042
dc.contributor.id1712399042
dc.contributor.id1711870042
dc.contributor.id1711462042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-07-01
dc.date.accessioned2025-07-01T09:41:05Z
dc.date.available2025-07-01T09:41:05Z
dc.date.issued2021
dc.description.abstractAll over the world, SAR operations are carried out to assist people in life-threatening situations. To search for a person in a life-threatening situation, the use of Unmanned Aerial Vehicles (UAVs) has increased drastically for different search and rescue missions to find the person at the earliest over the past few years. As UAVs are getting cheaper with advanced features like high-resolution cameras and long-lasting batteries, these devices are being used for autonomous search and rescue operations in different types of terrains and environments. These autonomous devices use artificial intelligence methods such as deep reinforcement learning algorithms for finding the optimal path and tracking the target. For marine-based environments, the target is continuously drifting with the ocean current, which makes it quite difficult for the UAV to search for the lost victim. In this project, we have made a simulation of a custom 2D marine environment and developed a dual memory architecture for finding the optimal path of a moving target to improve the learning of a UAV. We have incorporated our algorithm into popular deep reinforcement learning algorithms and improved the performance of classical algorithms by using our recycled experience replay. The results delineate that with a simple dual memory structure, immense progress in stable learning behavior can be obtained. The main goal of this project is to enhance the performance of prevalent deep reinforcement learning algorithms and test their performance in a simulated marine environment.
dc.description.degreeUndergraduate
dc.identifier.cd600000387
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1213
dc.language.isoen
dc.publisherNorth South University
dc.rights©Nsulibrary
dc.titleRER: Recycled Experience Replay with Dual Memory Architecture for Path Planning of a Moving Target using Deep Reinforcement Learning
dc.typeProject
oaire.citation.endPage77
oaire.citation.startPage1
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