Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Understand

We introduce ROLL, an efficient, scalable, and user-friendly library designed for Reinforcement Learning Optimization for Large-scale Learning.

• ROLL caters to three primary user groups: tech pioneers aiming for cost-effective, fault-tolerant large-scale training, developers requiring flexible control over training workflows, and researchers seeking agile experimentation.
• ROLL is built upon several key modules to serve these user groups effectively.
• First, a single-controller architecture combined with an abstraction of the parallel worker simplifies the development of the training pipeline.