Authors: Yiwei Shi, Mengyue Yang, Qi Zhang, Weinan Zhang, Cunjia Liu, Weiru Liu
Abstract: In many real-world scenarios, such as gas leak detection or environmental
pollutant tracking, solving the Inverse Source Localization and
Characterization problem involves navigating complex, dynamic fields with
sparse and noisy observations. Traditional methods face significant challenges,
including partial observability, temporal and spatial dynamics,
out-of-distribution generalization, and reward sparsity. To address these
issues, we propose a hierarchical framework that integrates Bayesian inference
and reinforcement learning. The framework leverages an attention-enhanced
particle filtering mechanism for efficient and accurate belief updates, and
incorporates two complementary execution strategies: Attention Particle
Filtering Planning and Attention Particle Filtering Reinforcement Learning.
These approaches optimize exploration and adaptation under uncertainty.
Theoretical analysis proves the convergence of the attention-enhanced particle
filter, while extensive experiments across diverse scenarios validate the
framework’s superior accuracy, adaptability, and computational efficiency. Our
results highlight the framework’s potential for broad applications in dynamic
field estimation tasks.
Source: http://arxiv.org/abs/2501.13084v1
