# Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods

arXiv: https://arxiv.org/abs/2003.11164 · HTML: https://mlsanity.com/paper/2003.11164/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2106.07551/) (2021)
- [SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores](https://mlsanity.com/paper/2306.16688/) (2023)
- [Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey](https://mlsanity.com/paper/2411.05614/) (2024)
- [Open-Ended Learning Strategies for Learning Complex Locomotion Skills](https://mlsanity.com/paper/2206.06796/) (2022)

## Abstract (arXiv)

Recent advances in machine learning are consistently enabled by increasing amounts of computation. Reinforcement learning (RL) and population-based methods in particular pose unique challenges for efficiency and flexibility to the underlying distributed computing frameworks. These challenges include frequent interaction with simulations, the need for dynamic scaling, and the need for a user interface with low adoption cost and consistency across different backends. In this paper we address these challenges while still retaining development efficiency and flexibility for both research and practical applications by introducing Fiber, a scalable distributed computing framework for RL and population-based methods. Fiber aims to significantly expand the accessibility of large-scale parallel computation to users of otherwise complicated RL and population-based approaches without the need to for specialized computational expertise.