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Abstract:
We'll introduce GPU-accelerated unsupervised reinforcement and auxiliary learning (UNREAL) algorithm. Recent state-of-the-art deep reinforcement learning algorithms, such as A3C and UNREAL, are designed to train on a single device with only CPUs. 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, we extend GA3C with the auxiliary tasks from UNREAL to create GUNREAL. We show that our GUNREAL system finished training faster than UNREAL and reached higher scores than GA3C.
We'll introduce GPU-accelerated unsupervised reinforcement and auxiliary learning (UNREAL) algorithm. Recent state-of-the-art deep reinforcement learning algorithms, such as A3C and UNREAL, are designed to train on a single device with only CPUs. 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, we extend GA3C with the auxiliary tasks from UNREAL to create GUNREAL. We show that our GUNREAL system finished training faster than UNREAL and reached higher scores than GA3C.  Back
 
Topics:
AI and DL Research, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8219
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