# ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning

**Authors:** Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, Michael I. Jordan  
**arXiv:** [2112.05923](https://arxiv.org/abs/2112.05923)  
**Date:** 2021-12-11  
**Summary page:** https://www.summarizepaper.com/en/paper/2112.05923v2/

## Key points

- Significant advancements in deep reinforcement learning (DRL) have been made in enhancing learning and actuation across various applications like game playing and robotic control.
- Widespread adoption of DRL for complex real-world problems faces a challenge due to the substantial cost associated with data collection, especially in generating transitions from agent-environment interactions.
- Leveraging a cloud-native approach to train DRL agents on a GPU cloud platform has emerged as a promising solution to address the challenge of high data collection costs.
- ElegantRL-podracer is a scalable and elastic library designed specifically for training DRL agents on GPU cloud platforms, enabling massively parallel training at multiple levels.
- The library employs a tournament-based ensemble scheme that orchestrates training processes on hundreds or thousands of GPUs by scheduling interactions between leaderboards and training pools comprising hundreds of pods.
- Each pod within ElegantRL-podracer operates by simulating agent-environment interactions in parallel, utilizing nearly 7,000 GPU CUDA cores within a single GPU.
- The design principles of ElegantRL-podracer prioritize high scalability, elasticity, and accessibility through containerization, microservices, and MLOps development methodologies.
- Extensive experiments using an NVIDIA DGX SuperPOD cloud infrastructure showed that ElegantRL-podracer outperformed RLlib—a notable reinforcement learning library—in terms of performance metrics across tasks related to locomotion and stock trading.

## Summary

In the realm of deep reinforcement learning (DRL), significant advancements have been made in enhancing learning and actuation across various applications such as game playing and robotic control. However, a persistent challenge in the widespread adoption of DRL for complex real-world problems lies in the substantial cost associated with data collection, particularly in generating transitions from agent-environment interactions. To address this challenge, leveraging a cloud-native approach to train DRL agents on a GPU cloud platform has emerged as a promising solution. has seen significant progress in improving learning and actuation across different fields like game playing and robotic control. However, one major hurdle for its widespread use in complex real-world problems is the high cost of data collection. This is especially true when it comes to generating transitions from agent-environment interactions. To overcome this challenge, researchers have turned to , utilizing GPU cloud platforms for training DRL agents. In this context, a groundbreaking contribution comes in the form of , a scalable and elastic library designed specifically for . This innovative library is engineered to efficiently support millions of GPU cores, enabling massively parallel training at multiple levels. At its core, ElegantRL-podracer employs a tournament-based ensemble scheme that orchestrates the training process on hundreds or even thousands of GPUs. This orchestration involves scheduling interactions between a leaderboard and a training pool comprising hundreds of pods. Delving deeper into the technical intricacies, each pod within ElegantRL-podracer operates by simulating agent-environment interactions in parallel, fully harnessing nearly 7,000 GPU CUDA cores within a single GPU. The library's design principles prioritize high scalability, elasticity, and accessibility through adherence to containerization, microservices, and MLOps development methodologies. To validate the efficacy of ElegantRL-podracer in practical scenarios, extensive experiments were conducted using an NVIDIA DGX SuperPOD cloud infrastructure across tasks related to locomotion and stock trading. The results showcased that ElegantRL-podracer significantly outperformed RLlib—a notable reinforcement learning library—in terms of performance metrics. The collaborative efforts of authors Xiao-Yang Liu, Zechu Li, Zhuoran Yang, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo, and Michael I. Jordan culminated in the development and presentation of ElegantRL-podracer at the Deep Reinforcement Learning Workshop held during NeurIPS 2021. With its comprehensive features and superior performance demonstrated through  on diverse tasks, ElegantRL-podracer stands as a cutting-edge tool for advancing research and applications in deep reinforcement learning. Interested individuals can access the codebase for ElegantRL-podracer on GitHub for further exploration and utilization.