This is the Scilab-RL repository focusing on goal-conditioned reinforcement learning using the stable baselines 3 methods and Gymnasium interface.

The framework is tailored towards the rapid prototyping, development and evaluation of new RL algorithms and methods. It has the following unique selling-points compared to others, like spinning up and stable baselines:

• Built-in data visualization for fast and efficient debugging using MLFLow and Weights & Biases
• Support for many state-of-the-art algorithms via stable baselines 3 and extensible to others
• Built-in hyperparameter optimization using Optuna
• Easy development of new robotic simulation and real robot environments based on MuJoCo
• Smoke and performance testing
• Compatibility between a multitude of state-of-the-art algorithms for quick empirical comparison and evaluation
• A focus on goal-conditioned reinforcement learning with hindsight experience replay to avoid environment-specific reward shaping