Friday, November 22, 2024 - 3:10 p.m. to 4 p.m.
Chase Ocean Engineering Lab
Abstract:
The computational resources
required for deep reinforcement learning (RL) model training are
challenging for many applications with computational resource constraints.
Low-precision methods are a promising avenue to alleviate computational
bottlenecks and reduce the time, memory, and power required for model training.
A notional drawback to low-precision training methods is that the
benefits depend on model complexity and Graphical Processing Unit (GPU)
hardware capabilities. In this work, we will address this notion and gap
in the literature and investigate how the benefits of low-precision training
methods scale with GPU capabilities and model complexity by benchmarking GPU
resource usage using state-of-the-art system profiling methods for Soft Actor-Critic
(SAC) deep RL agents trained on control environments for a range of model
configurations and NVidia GPUs spanning from edge devices to data science grade
desktop cards.
