LLM Behavioral Cloning

Under development.

Requirements

(Highly recommended) Make a virtual environment:
- virtualenv venv
- source venv/bin/activate
Clone this repository with submodules: git clone https://github.com/tomsilver/llm-pddl
Run pip install -e .[develop] to install the main dependencies for development.
Install Fast Downward and export FD_EXEC_PATH="<your absolute path here>/downward"
Obtain an API key from OpenAI for Codex access and export OPENAI_API_KEY=<your API key>

You can't push directly to master. Make a new branch in this repository (don't use a fork, since that will not properly trigger the checks when you make a PR). When your code is ready for review, make a PR and request reviews from the appropriate people.
To merge a PR, you need at least one approval, and you have to pass the 4 checks defined in .github/workflows/llmclone.yml, which you can run locally as follows:
- pytest -s tests/ --cov-config=.coveragerc --cov=llmclone/ --cov=tests/ --cov-fail-under=100 --cov-report=term-missing:skip-covered --durations=0
- mypy .
- pytest . --pylint -m pylint --pylint-rcfile=.llmclone_pylintrc
- ./run_autoformat.sh
The first one is the unit testing check, which verifies that unit tests pass and that code is adequately covered. The "100" means that all lines in every file must be covered.
The second one is the static typing check, which uses Mypy to verify type annotations.
The third one is the linter check, which runs Pylint with the custom config file .llmclone_pylintrc in the root of this repository. Feel free to edit this file as necessary.
The fourth one is the autoformatting check, which uses the custom config files .style.yapf and .isort.cfg in the root of this repository.