Catalyst-RL

Catalyst.RL is a PyTorch framework designed to accelerate reinforcement learning research and development. It enables researchers to focus on novel ideas by providing a structured, reusable, and reproducible pipeline, eliminating the need to write boilerplate training loops.

Key Features

• Universal Training Loop — Provides a standardized training and inference loop with built-in metrics, early stopping, and model checkpointing.
• Configuration-Driven — Uses configuration files to manage model and data hyperparameters for easy experimentation.
• Reproducibility — Automatically saves source code and environment variables to ensure experiment reproducibility.
• Callback System — Offers reusable components to customize and extend the training pipeline.
• Distributed RL Algorithms — Includes implementations of popular model-free reinforcement learning algorithms with distributed training support.
• Best Practices Integration — Incorporates PyTorch best practices such as SWA, AdamW, Ranger optimizer, OneCycle, and FP16 training.

Philosophy

Catalyst.RL is built on the philosophy of breaking the cycle of writing repetitive training code, allowing researchers to concentrate on developing new ideas rather than infrastructure.