GA3C: GPU-based A3C for Deep Reinforcement Learning
NVIDIA
arXiv:1611.06256 [cs.LG], (18 Nov 2016)
@article{babaeizadeh2016gpubased,
title={GA3C: GPU-based A3C for Deep Reinforcement Learning},
author={Babaeizadeh, Mohammad and Frosio, Iuri and Tyree, Stephen and Clemons, Jason and Kautz, Jan},
year={2016},
month={nov},
archivePrefix={"arXiv"},
primaryClass={cs.LG}
}
We introduce and analyze the computational aspects of a hybrid CPU/GPU implementation of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. Our analysis concentrates on the critical aspects to leverage the GPU’s computational power, including the introduction of a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. We also show the potential for the use of larger DNN models on a GPU. Our TensorFlow implementation achieves a significant speed up compared to our CPU-only implementation, and it will be made publicly available to other researchers.
November 23, 2016 by hgpu
