Enhanced Reward Function Design for Source Term Estimation Based on Deep Reinforcement Learning

Enhanced Reward Function Design for Source Term Estimation Based on Deep Reinforcement Learning

This study investigates the design of reward functions for deep reinforcement learning-based source term estimation (STE). Estimating the properties of unknown hazardous gas leakage using a mobile sensor, known as STE problems, is challenging due to environmental turbulence and sensor noise. To addr...

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Bibliographic Details
Main Authors: Junhee Lee, Hongro Jang, Minkyu Park, Hyondong Oh
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Source term estimation
deep reinforcement learning
deep Q-network
reward function
Bayesian inference
particle filter
Online Access:https://ieeexplore.ieee.org/document/11004010/
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