Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes

About

We present a study in Distributed Deep Reinforcement Learning (DDRL) focused on scalability of a state-of-the-art Deep Reinforcement Learning algorithm known as Batch Asynchronous Advantage ActorCritic (BA3C). We show that using the Adam optimization algorithm with a batch size of up to 2048 is a viable choice for carrying out large scale machine learning computations. This, combined with careful reexamination of the optimizer's hyperparameters, using synchronous training on the node level (while keeping the local, single node part of the algorithm asynchronous) and minimizing the memory footprint of the model, allowed us to achieve linear scaling for up to 64 CPU nodes. This corresponds to a training time of 21 minutes on 768 CPU cores, as opposed to 10 hours when using a single node with 24 cores achieved by a baseline single-node implementation.

Igor Adamski, Robert Adamski, Tomasz Grel, Adam Jędrych, Kamil Kaczmarek, Henryk Michalewski · 2018

Related benchmarks

Task	Dataset	Result
Reinforcement Learning	Atari 2600 SpaceInvaders	Best Stable Score650	7
Reinforcement Learning	Atari 2600 Pong	Best Score20	4
Reinforcement Learning	Atari 2600 Breakout	Best Stable Score350	4
Reinforcement Learning	Atari 2600 Seaquest	Best Stable Score1832	4
Reinforcement Learning	Atari 2600 BeamRider	Best Stable Score14900	3
Reinforcement Learning	Atari 2600 Boxing	Best Stable Score98	2