{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Stable Baselines3 provides PyTorch implementations of reinforcement learning algorithms designed to work with Gymnasium environments, following a scikit-learn-like API for training and inference.","skillfed_tags":["reinforcement-learning","pytorch","agent-training"],"use_cases":["Train a reinforcement learning agent on a custom or standard Gymnasium environment for robotics or game-playing tasks.","Establish a baseline for comparing new RL algorithms or techniques against well-tested implementations.","Prototype RL solutions quickly using pre-built algorithms without implementing training loops from scratch.","Conduct RL research with a modular, extensible codebase that separates policy, value, and environment concerns.","Deploy trained RL policies in production systems where reproducibility and reliability are critical."],"what_it_does":"Stable Baselines3 is a collection of production-ready reinforcement learning algorithms implemented in PyTorch. It provides a unified, scikit-learn-inspired interface for training RL agents on Gymnasium environments, making it accessible for both researchers and practitioners. The library abstracts away low-level implementation details while maintaining flexibility for customization.\n\nThe package is built on top of gymnasium, numpy, torch, and cloudpickle, handling the complexity of policy networks, value functions, and training loops so users can focus on environment design and hyperparameter tuning. It supports algorithms like PPO and DQN and is designed as a foundation for building new RL approaches or comparing novel methods against established baselines.","worth_installing":"Yes. Stable Baselines3 is actively maintained, has no known vulnerabilities, installs with low friction, and is widely used (top 5000 PyPI packages). It's the standard choice for RL practitioners who want reliable algorithm implementations without reinventing core components. Install it if you're training RL agents or need a trusted baseline for comparison."},"id":"stable-baselines3","links":{"html":"https://skillfed.io/packages/stable-baselines3","md":"https://skillfed.io/packages/stable-baselines3.md","pypi":"https://pypi.org/project/stable-baselines3/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-15","license_spdx":null,"license_treatment":"permissive","name":"stable-baselines3","python_support":"supports_current","summary":"Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms."},"popularity":{"monthly_downloads":1412536,"position":3934,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.9.0"}