GPU-Accelerated Atari Emulation for Reinforcement Learning

Steven Dalton, Iuri Frosio, Michael Garland
arXiv:1907.08467 [cs.LG], (19 Jul 2019)


   title={GPU-Accelerated Atari Emulation for Reinforcement Learning},

   author={Steven Dalton and Iuri Frosio and Michael Garland},






We designed and implemented a CUDA port of the Atari Learning Environment (ALE), a system for developing and evaluating deep reinforcement algorithms using Atari games. Our CUDA Learning Environment (CuLE) overcomes many limitations of existing CPU-based Atari emulators and scales naturally to multi-GPU systems. It leverages the parallelization capability of GPUs to run thousands of Atari games simultaneously; by rendering frames directly on the GPU, CuLE avoids the bottleneck arising from the limited CPU-GPU communication bandwidth. As a result, CuLE is able to generate between 40M and 190M frames per hour using a single GPU, a finding that could be previously achieved only through a cluster of CPUs. We demonstrate the advantages of CuLE by effectively training agents with traditional deep reinforcement learning algorithms and measuring the utilization and throughput of the GPU. Our analysis further highlights the differences in the data generation pattern for emulators running on CPUs or GPUs. CuLE is available.
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