Atari games and Intel processors

About

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.

Robert Adamski, Tomasz Grel, Maciej Klimek, Henryk Michalewski · 2017

Related benchmarks

Task	Dataset	Result
Reinforcement Learning	Atari 2600 SpaceInvaders	Best Stable Score700	7
Reinforcement Learning	Atari 2600 Breakout	Best Stable Score400	4
Reinforcement Learning	Atari 2600 Seaquest	Best Stable Score1840	4
Reinforcement Learning	Atari 2600 Pong	Best Score17	4