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

Understand

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.