We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and concentrate on aspects critical to leveraging the GPU's computational power. We introduce a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. Our hybrid CPU/GPU version of A3C, based on TensorFlow, achieves a significant speed up compared to a CPU implementation; we make it publicly available to other researchers at https://github.com/NVlabs/GA3C .
@article{arxiv.1611.06256,
title = {Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU},
author = {Mohammad Babaeizadeh and Iuri Frosio and Stephen Tyree and Jason Clemons and Jan Kautz},
journal= {arXiv preprint arXiv:1611.06256},
year = {2017}
}