# Performing Deep Recurrent Double Q-Learning for Atari Games

arXiv: https://arxiv.org/abs/1908.06040 · HTML: https://mlsanity.com/paper/1908.06040/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Deep Q-network using reservoir computing with multi-layered readout](https://mlsanity.com/paper/2203.01465/) (2022)
- [Subequivariant Reinforcement Learning Framework for Coordinated Motion Control](https://mlsanity.com/paper/2403.15100/) (2024)
- [Modeling and Optimization of Epidemiological Control Policies Through Reinforcement Learning](https://mlsanity.com/paper/2402.06640/) (2024)
- [Inferring Discussion Topics about Exploitation of Vulnerabilities from Underground Hacking Forums](https://mlsanity.com/paper/2405.04561/) (2024)
- [Improving Experience Replay through Modeling of Similar Transitions' Sets](https://mlsanity.com/paper/2111.06907/) (2021)

## Abstract (arXiv)

Currently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, We proposed Deep Recurrent Double Q-Learning that is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.