This module integrates Robust Online Monitoring methods with Reinforcement Learning stuff. The first motivation is testing/monitoring RL models.
Those are needed for building some of the required python modules:
- CMake
- swig
Then run the following:
pip install -r requirements.txt
Run python run_app.py
, then open browser.
- Select an environment among a list of supported ones.
- To load a model, choose between
- Random: random actions
- Local: Upload model zip files created with stable-baselines-3, then choose one
- Hugging Face: Fetch the list of models available on Hugging Face, then choose one
- Choose between running with or without human render
- Runs from a list of seeds and store traces
- Compute total rewards
- Plots observation, reward, actions, individually or together of any trace, with flexible layout
- Evaluate (monitor) and plot quantitative and Boolean satisfaction of any Signal Temporal Logic formula (STL)
- Sort runs against STL formula robustness