GUNREAL: GPU-accelerated UNsupervised REinforcement and Auxiliary Learning

Koichi Shirahata, Youri Coppens, Takuya Fukagai, Yasumoto Tomita, Atsushi Ike


Recent state-of-the-art deep reinforcement learning algorithms, such as A3C and UNREAL, are designed to train on a single device with only CPU's. Using GPU acceleration for these algorithms results in low GPU utilization, which means the full performance of the GPU is not reached. Motivated by the architecture changes made by the GA3C algorithm, which gave A3C better GPU acceleration, together with the high learning efficiency of the UNREAL algorithm, this paper extends GA3C with the auxiliary tasks from UNREAL to create a deep reinforcement learning algorithm, GUNREAL, with higher learning efficiency and also benefiting from GPU acceleration. We show that our GUNREAL system achieves 3.8 to 9.5 times faster training speed compared to UNREAL and 73% more training efficiency compared to GA3C.


Deep Reinforcement Learning; GPU; Auxiliary Tasks; Deep Learning

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