gym-saturation 0.3.11

An OpenAI Gym environment for saturation provers

gym-saturation

gym-saturation is an OpenAI Gym environment for reinforcement learning (RL) agents capable of proving theorems. Currently, only theorems written in TPTP library formal language in clausal normal form (CNF) are supported. gym-saturation implements the ‘given clause’ algorithm (similar to one used in Vampire and E Prover). Being written in Python, gym-saturation was inspired by PyRes. In contrast to monolithic architecture of a typical Automated Theorem Prover (ATP), gym-saturation gives different agents opportunities to select clauses themselves and train from their experience. Combined with a particular agent, gym-saturation can work as an ATP.

gym-saturation can be interesting for RL practitioners willing to apply their experience to theorem proving without coding all the logic-related stuff themselves. It also can be useful for automated deduction researchers who want to create an RL-empowered ATP.

How to Install

The best way to install this package is to use pip:

pip install gym-saturation

Another option is to use conda:

conda install -c conda-forge gym-saturation

One can also run it in a Docker container:

docker build -t gym-saturation https://github.com/inpefess/gym-saturation.git
docker run -it --rm -p 8888:8888 gym-saturation jupyter-lab --ip=0.0.0.0 --port=8888

How to use

import gym
import os

env = gym.make("GymSaturation-v0", problem_list=["..."])
observation = env.reset()
observation, reward, done, info = env.step(action)

See the notebook or run it in Binder for more information.

How to Contribute

Pull requests are welcome. To start:

git clone https://github.com/inpefess/gym-saturation
cd gym-saturation
# activate python virtual environment with Python 3.7+
pip install -U pip
pip install -U setuptools wheel poetry
poetry install
# recommended but not necessary
pre-commit install

All the tests in this package are doctests. One can run them with the following command:

pytest --doctest-modules gym-saturation

To check the code quality before creating a pull request, one might run the script local-build.sh. It locally does nearly the same as the CI pipeline after the PR is created.

Reporting issues or problems with the software

Questions and bug reports are welcome on the tracker.

More documentation

More documentation can be found here.