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

TL;DR

This paper demonstrates that distributed deep reinforcement learning can be scaled efficiently using BA3C with Adam optimizer, achieving a 30-fold speedup in training Atari games from 10 hours to 21 minutes across 64 CPU nodes.

Contribution

The study introduces a scalable distributed reinforcement learning setup with optimized hyperparameters, enabling rapid training of Atari agents on large CPU clusters.

Findings

Linear scaling achieved up to 64 CPU nodes.

Training time reduced from 10 hours to 21 minutes.

Effective use of Adam optimizer with large batch sizes.

Abstract

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.