mlsanityData to 14 Oct 2026

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

arXiv 2408.00930 ↗First posted 1 Aug 2024cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 27 references that are in the map. First posted on arXiv 1 Aug 2024.

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11 of 27 references.

References · 11

  1. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  2. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  3. 2021Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulationcited by 407
  4. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  5. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213
  6. 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
  7. 2019Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policiescited by 124
  8. 2021Podracer architectures for scalable Reinforcement Learningcited by 38
  9. 2021Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economistcited by 23
  10. 2021WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPUcited by 10
  11. 2023Hint assisted reinforcement learning: an application in radio astronomycited by 2

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