# Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation

arXiv: https://arxiv.org/abs/2605.14774 · HTML: https://mlsanity.com/paper/2605.14774/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 4 of 4)

- [Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control](https://mlsanity.com/paper/2110.13450/) (2021)
- [Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis](https://mlsanity.com/paper/2110.13661/) (2021)
- [Forecasting battery capacity and power degradation with multi-task learning](https://mlsanity.com/paper/2111.14937/) (2021)
- [Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2209.08112/) (2022)

## Abstract (arXiv)

In the world of AI and advanced technologies investigation aspects identification of a crime or criminal plays a major problem. In this research we focus on a Conventional ways of implicating criminal investigations usually rely on limited data analysis. Finding an optimal and efficient method that will effectively identify criminals from complex datasets and minimise false positives and false negatives is the considered as a challenge. The main novelty approach of this work is based on the deep learning algorithm Deep Deterministic Policy Gradient (DDPG) is presented in this paper. We train the DDPG model with a dataset of crime scene material, witness statements and suspect profiles. The algorithm uses features to maximise the likelihood of identifying the offender while minimising the noise impact and irrelevant data. We show the efficacy of the proposed method, where DDPG identified criminals with an amazing accuracy of 95% than other several existing methods.