Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research

TL;DR

WarpSci is a GPU-based framework that significantly enhances data throughput for reinforcement learning, enabling thousands of simulations concurrently without CPU-GPU data transfer, thus accelerating scientific research involving complex environments.

Contribution

The paper presents WarpSci, a novel domain-agnostic framework that eliminates CPU-GPU data transfer bottlenecks, allowing high-throughput reinforcement learning on GPUs for complex scientific applications.

Findings

Enables thousands of concurrent simulations on GPUs.

Reduces data transfer bottlenecks between CPU and GPU.

Accelerates data-driven scientific research workflows.

Abstract

We introduce WarpSci, a domain agnostic framework designed to overcome crucial system bottlenecks encountered in the application of reinforcement learning to intricate environments with vast datasets featuring high-dimensional observation or action spaces. Notably, our framework eliminates the need for data transfer between the CPU and GPU, enabling the concurrent execution of thousands of simulations on a single or multiple GPUs. This high data throughput architecture proves particularly advantageous for data-driven scientific research, where intricate environment models are commonly essential.