# Belief-Contraction-Driven Active Inverse Source Localization and Characterization

arXiv: https://arxiv.org/abs/2501.13084 · HTML: https://mlsanity.com/paper/2501.13084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 22 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimation](https://mlsanity.com/paper/2409.09541/) (2024)
- [Dual Control for Exploitation and Exploration (DCEE) in Autonomous Search](https://mlsanity.com/paper/2012.06276/) (2020)
- [Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation](https://mlsanity.com/paper/2108.08062/) (2021)

## Built on by (most cited first, 1 of 1)

- [Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields](https://mlsanity.com/paper/2604.26095/) (2026)

## Abstract (arXiv)

Active inverse source localization and characterization (ISLC) in dynamic fields requires sequential decision making under partial observability, where a mobile sensor must infer latent source parameters from sparse, noisy readings. We introduce a belief-contraction-driven approach that unifies inference, stopping, and control. An attention-augmented particle filter stabilizes Bayesian belief updates through ESS-based resampling, feature-aware sparse attention smoothing, and Metropolis-Hastings rejuvenation that preserves the filtering posterior. Belief contraction (posterior dispersion) defines both a termination rule and a goal-aligned intrinsic reward, enabling reinforcement learning without distance-to-source shaping. Across seven field modalities, spatial out-of-distribution tests, and nonstationary source shifts, our agent (ATT-PFRL) achieves higher completion, faster convergence, and more accurate localization than planning and RL+Bayes baselines under similar computation. Fixed-trajectory studies also show improved ESS and lower RMSE, isolating the benefit of the inference layer.