Learning rate in the reinforcement learning method for unknown location targets searching system

Learning rate in the reinforcement learning method for unknown location targets searching system

The object of study is a system with a different number of mutually independent modules in reinforcement learning. The article reviews the research related to reinforcement learning and the need to determine the dependence of the learning rate of the object of study on the number of mutually indepen...

Full description

Bibliographic Details
Main Authors:	Y. Albrekht, A. Писаренко
Format:	Article
Language:	English
Published:	Igor Sikorsky Kyiv Polytechnic Institute 2023-05-01
Series:	Adaptivni Sistemi Avtomatičnogo Upravlinnâ
Subjects:	reinforcement learning mutually independent modules deep q learning
Online Access:	http://asac.kpi.ua/article/view/278916

Similar Items

Unknown location targets searching system in known environment using reinforcement learning
by: Y. Albrekht, et al.
Published: (2023-05-01)

Target-Network Update Linked with Learning Rate Decay Based on Mutual Information and Reward in Deep Reinforcement Learning
by: Chayoung Kim
Published: (2023-09-01)

UAV Autonomous Target Search Based on Deep Reinforcement Learning in Complex Disaster Scene
by: Chunxue Wu, et al.
Published: (2019-01-01)

Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning
by: Qiming Yang, et al.
Published: (2020-01-01)

Timeslot Scheduling with Reinforcement Learning Using a Double Deep Q-Network
by: Jihye Ryu, et al.
Published: (2023-02-01)

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
by: Abdikarim Mohamed Ibrahim, et al.
Published: (2021-11-01)

Deep Reinforcement Learning for Drone Delivery
by: Guillem Muñoz, et al.
Published: (2019-09-01)

Target Search Control of AUV in Underwater Environment With Deep Reinforcement Learning
by: Xiang Cao, et al.
Published: (2019-01-01)

SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification
by: Sunil Kumar Prabhakar, et al.
Published: (2022-01-01)

Reinforcement Learning Models and Algorithms for Diabetes Management
by: Kok-Lim Alvin Yau, et al.
Published: (2023-01-01)

Research on Microseismic Source Locations Based on Deep Reinforcement Learning
by: Yangxu Wu, et al.
Published: (2019-01-01)

An Adaptive Threshold for the Canny Algorithm With Deep Reinforcement Learning
by: Keong-Hun Choi, et al.
Published: (2021-01-01)

Online intelligent maneuvering penetration methods of missile with respect to unknown intercepting strategies based on reinforcement learning
by: Yaokun Wang, et al.
Published: (2022-09-01)

Traffic Signal Control System Using Deep Reinforcement Learning With Emphasis on Reinforcing Successful Experiences
by: Naoki Kodama, et al.
Published: (2022-01-01)

Feature Selection for Malware Detection Based on Reinforcement Learning
by: Zhiyang Fang, et al.
Published: (2019-01-01)

Home Energy Management Algorithm Based on Deep Reinforcement Learning Using Multistep Prediction
by: Naoki Kodama, et al.
Published: (2021-01-01)

Manipulating XXY Planar Platform Positioning Accuracy by Computer Vision Based on Reinforcement Learning
by: Yi-Cheng Huang, et al.
Published: (2023-03-01)

Efficient Novelty Search Through Deep Reinforcement Learning
by: Longxiang Shi, et al.
Published: (2020-01-01)

Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters
by: Shuxian Sun, et al.
Published: (2022-11-01)

Hierarchical Reinforcement Learning: A Survey and Open Research Challenges
by: Matthias Hutsebaut-Buysse, et al.
Published: (2022-02-01)

Measuring and characterizing generalization in deep reinforcement learning
by: Sam Witty, et al.
Published: (2021-12-01)

An autonomous radiation source detection policy based on deep reinforcement learning with generalized ability in unknown environments
by: Hao Hu, et al.
Published: (2023-01-01)

Deep Reinforcement Learning Hands-On /
by: Lapan, Maxim 655254, author
Published: (2020)

Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning
by: Che-Cheng Chang, et al.
Published: (2020-06-01)

Obstacle-Aware Navigation of Soft Growing Robots via Deep Reinforcement Learning
by: Haitham El-Hussieny, et al.
Published: (2024-01-01)

Guaranteed hierarchical reinforcement learning
by: Ang, Riley Xile
Published: (2024)

Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning
by: Junjie Zeng, et al.
Published: (2019-09-01)

Deep Reinforcement Learning-Based Resource Management in Maritime Communication Systems
by: Xi Yao, et al.
Published: (2024-03-01)

Microgrid energy management using deep Q-network reinforcement learning
by: Mohammed H. Alabdullah, et al.
Published: (2022-11-01)

Learning Mobile Manipulation through Deep Reinforcement Learning
by: Cong Wang, et al.
Published: (2020-02-01)

Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates
by: Ji-Heon Park, et al.
Published: (2024-01-01)

Reinforcement Learning Approaches to Optimal Market Making
by: Bruno Gašperov, et al.
Published: (2021-10-01)

UAV Path Planning and Obstacle Avoidance Based on Reinforcement Learning in 3D Environments
by: Guan-Ting Tu, et al.
Published: (2023-01-01)

Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning
by: Yuguang Yang, et al.
Published: (2020-01-01)

Reinforcement Learning Approach for Adaptive C-V2X Resource Management
by: Teguh Indra Bayu, et al.
Published: (2023-10-01)

The application of reinforcement learning to NATM tunnel design
by: Enrico Soranzo, et al.
Published: (2022-12-01)

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

Generating 2D Lego Compatible Puzzles Using Reinforcement Learning
by: Cheolseong Park, et al.
Published: (2020-01-01)

Offline Meta-Reinforcement Learning with Contrastive Prediction
by: HAN Xu, WU Feng
Published: (2023-08-01)

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