# Reinforcement Learning and Video Games

arXiv: https://arxiv.org/abs/1909.04751 · HTML: https://mlsanity.com/paper/1909.04751/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

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

- [Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions](https://mlsanity.com/paper/2210.11137/) (2022)

## Abstract (arXiv)

Reinforcement learning has exceeded human-level performance in game playing AI with deep learning methods according to the experiments from DeepMind on Go and Atari games. Deep learning solves high dimension input problems which stop the development of reinforcement for many years. This study uses both two techniques to create several agents with different algorithms that successfully learn to play T-rex Runner. Deep Q network algorithm and three types of improvements are implemented to train the agent. The results from some of them are far from satisfactory but others are better than human experts. Batch normalization is a method to solve internal covariate shift problems in deep neural network. The positive influence of this on reinforcement learning has also been proved in this study.