# Atari games and Intel processors

arXiv: https://arxiv.org/abs/1705.06936 · HTML: https://mlsanity.com/paper/1705.06936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2017.

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

- [Scaling up Stochastic Gradient Descent for Non-convex Optimisation](https://mlsanity.com/paper/2210.02882/) (2022)

## Abstract (arXiv)

The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes them exceptionally suitable for CPU computations. However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions. In this work we present our results on learning strategies in Atari games using a Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0 machine learning framework. We also analyze effects of asynchronous computations on the convergence of reinforcement learning algorithms.