RLROM is a library for testing and training reinforcement learning agent using online monitoring of signal temporal logics formulas, and more.
Project description
RLRom
This module integrates Robust Online Monitoring methods with Reinforcement Learning stuff. The motivation is first to test RL agents using interpretable monitors, then use these monitors to train models to perform complex tasks, and/or converge toward behaviors that reliably satisfy certain requirements.
Install
Those are needed for building some of the required python modules, in particular stlrom for STL monitoring.
- CMake
- swig
Then installing should be as simple as
pip install rlrom
Getting Started
RLRom reads configuration files in the YAML format as inputs. Examples are provided in the examples folder. A command line interface is provided through the script rlrom_run. For instance,
$ rlrom_run test examples/cartpole/cfg_cartpole.cfg
will run a few episode of the cartpole classic environment, fetching a model on huggingface and monitor a formula on these episodes.
More programmatic features are demonstrated in notebooks, in particular this notebook which presents a case study around highway-env environment.
Project details
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