Stable-Baselines3 is an open-source Python library for reinforcement learning research and development. It provides reliable, well-tested implementations of popular RL algorithms (built on PyTorch), utilities for training and evaluation, and easy integration with simulation environments like OpenAI Gym / Gymnasium. Stable-Baselines3 is lightweight and designed for both experimentation and production workflows.

Features of Stable-Baselines3:

• Implementations of modern RL algorithms (PPO, A2C, DQN, SAC, TD3, etc.).
• Built on PyTorch for flexibility and performance.
• Easy integration with OpenAI Gym / Gymnasium environments and custom environments.
• Well-documented API and examples for fast prototyping and research.
• Utilities for training, evaluation, model saving/loading, and logging.
• Lightweight (runs from CLI / scripts inside virtual environments; no web server required).
• Extensible — easy to add custom policies, callbacks, and environments.

To check Stable-Baselines3 installation :

$ cd /opt/stable3_env

$ source venv/bin/activate

$ python -c "import stable_baselines3 as sb3; print(sb3.__version__)"

Disclaimer: Stable-Baselines3 is open-source software provided under the MIT license. It is offered "as is," without any warranty, express or implied. Users are responsible for ensuring the ethical and legal use of Stable-Baselines3 in their projects.