Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes
This work addresses the problem of slow training times in deep reinforcement learning for researchers and practitioners, representing an incremental improvement in distributed computing efficiency.
The study tackled the scalability of the BA3C deep reinforcement learning algorithm for Atari games, achieving a training time of 21 minutes on 768 CPU cores compared to 10 hours on a single node.
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.