reinforcement-learning

What problem does it solve?

Reinforcement learning projects rely on multiple libraries and inconsistent experiments; this guide consolidates best practices for Python RL across SB3, RLlib, and Gymnasium to improve reliability, reproducibility, and deployment readiness.

Core Features & Use Cases

• Guidance on library selection (SB3, RLlib, Gymnasium) for prototyping and production
• Practical workflows for training, debugging, hyperparameter tuning, evaluation, and reproducibility
• Production deployment strategies including monitoring and validation

Quick Start

Run a baseline PPO training on CartPole-v1 using the recommended workflow and evaluate stability.