stable-baselines3

To train reinforcement learning agents with Stable Baselines3, use a unified workflow that supports algorithms like PPO, SAC, A2C, TD3, and DQN. You can run training across parallel vectorized environments and save the trained model for later use.

Creating custom Gymnasium environments for RL experiments involves defining your environment logic and integrating evaluation hooks. You can then deploy these custom environments using vectorized setups to parallelize training with Stable Baselines3 algorithms.

Yes, you need Python with Gymnasium, Stable Baselines3, and NumPy installed to use vectorized environments. Optional dependencies like VecNormalize and VecVideoRecorder are available for advanced training and monitoring workflows.

Model persistence in Stable Baselines3 allows you to save trained RL agents to disk and reload them later. This ensures reproducible experiments by maintaining the agent's learned state and configuration across sessions.

Yes, you can integrate callbacks and monitoring into reinforcement learning training workflows. Stable Baselines3 supports adding custom callbacks to track performance, evaluate agents during training, and trigger specific actions based on training metrics.

Yes, example scripts and references are provided in the scripts/ and references/ directories. These offer algorithm-specific guidance for deploying DQN, TD3, and other RL agents using the Stable Baselines3 framework.