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GTC ON-DEMAND

Deep Learning & AI Frameworks
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CuLE : A Companion Library for Accelerated RL Training
Abstract:
Traditional RL training is dominated by experience collection processes executing on the CPU. However, this CPU oriented design pattern limits the utility of DL accelerators, such as GPUs. In this talk we present CuLE (cuda learning environment), an experimental deep RL companion library, to facilitate the generation of RL updates directly on the GPU. CuLE provides an implementation of ALE (atari learning environment), a challenging RL benchmark for discrete episodic tasks, executing directly on the GPU with the number of environments ranging from a few hundred to several thousand. Although traditional deep RL implementations use 12-16 agents coupled with replay memory to achieve training efficiency CuLE can generate a massive number of samples per step and supports new training scenarios that minimize expensive data movement operations. With 1024 agents CuLE achieves an 8-10x performance improvement by executing directly on the GPU compared to 1024 agents running in parallel on a 12-core CPU. We plan to extend CuLE to support a new set GPU-centric deep RL training schemes and new challenging training environments through integration with GFN.?
 
Topics:
Deep Learning & AI Frameworks, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8440
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