alterlab-stable-baselines3

To set up a Stable Baselines3 training pipeline in a Gymnasium environment, use the included train_rl_agent.py script, which provides ready-to-run templates for configuring and launching PPO training loops on standard environments like CartPole-v1.

Yes, Stable Baselines3 supports training custom reinforcement learning environments by providing ready-to-run templates and domain-focused references that standardize the integration and configuration of custom Gymnasium environments into your training pipeline.

The best way to evaluate a trained reinforcement learning agent using SB3 is running the evaluate_agent.py script, which standardizes the evaluation workflow and provides domain-focused utilities for assessing agent performance within Gymnasium environments.

Yes, this SB3 workflow supports vectorized training for reinforcement learning experiments by providing specific domain-focused references and utilities designed to standardize and optimize vectorized training pipelines within Gymnasium environments.

To run Stable Baselines3 reinforcement learning workflows, you need the gymnasium, stable-baselines3, and numpy dependencies installed in your Python environment to ensure full compatibility with the provided training and evaluation scripts.

You should use Stable Baselines3 for prototyping when you need production-ready workflows with standardized templates, vectorized training pipelines, and built-in evaluation utilities for benchmarking algorithms and teaching RL concepts in Gymnasium.