# Deep Q-Network for Stochastic Process Environments

arXiv: https://arxiv.org/abs/2308.03316 · HTML: https://mlsanity.com/paper/2308.03316/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)

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

- [An Automatic Detection Method for Hematoma Features in Placental Abruption Ultrasound Images Based on Few-Shot Learning](https://mlsanity.com/paper/2510.21495/) (2025)

## Abstract (arXiv)

Reinforcement learning is a powerful approach for training an optimal policy to solve complex problems in a given system. This project aims to demonstrate the application of reinforcement learning in stochastic process environments with missing information, using Flappy Bird and a newly developed stock trading environment as case studies. We evaluate various structures of Deep Q-learning networks and identify the most suitable variant for the stochastic process environment. Additionally, we discuss the current challenges and propose potential improvements for further work in environment-building and reinforcement learning techniques.