Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

We introduce a hybrid CPU/GPU version of the Asynchronous Advantage ActorCritic
(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


Mohammad Babaeizadeh (University of Illinois at Urbana-Champaign)

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