# Performing Deep Recurrent Double Q-Learning for Atari Games

## TL;DR

This paper introduces Deep Recurrent Double Q-Learning, combining Double Q-Learning with Recurrent Networks like LSTM for improved learning in Atari game environments.

## Contribution

It proposes a novel integration of Double Q-Learning with Recurrent Neural Networks, specifically LSTM, for enhanced deep reinforcement learning performance.

## Key findings

- Demonstrates improved learning stability in Atari games
- Shows effectiveness of combining Double Q-Learning with RNNs
- Provides a new approach for deep reinforcement learning in sequential tasks

## Abstract

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.