pulkitverma25 / predicators

Learning for effective and efficient bilevel planning

predicators

Repository Description

This codebase implements a framework for bilevel planning with learned neuro-symbolic relational abstractions, as described in this paper. Several features are concurrently under active development. Please contact [e-mail] and [e-mail] before attempting to use it for your own research. In particular, this codebase aims to ultimately provide an integrated system for learning the ingredients of search-then-sample bilevel planning with learned abstractions. That includes: options, predicates, operators, and samplers.

Code Structure

In src/, the environments are defined in the envs/ directory, and the approaches (both learning-based and not) are defined in the approaches/ directory. The core NSRT learning algorithm happens in src/nsrt_learning/nsrt_learning_main.py, which has the following steps:

• Segment data based on changes in predicates.
• Learn how many NSRTs we need, along with the symbolic operator components of each (parameters, preconditions, and effects).
• Learn options and annotate data with them.
• Learn samplers.
• Finalize the NSRTs.

Methods for predicate learning are implemented as Approaches (e.g., src/approaches/grammar_search_invention_approach.py), and may interface with the core structure of src/nsrt_learning/nsrt_learning_main.py in various ways.

A simple implementation of search-then-sample bilevel planning is provided in src/planning.py. This implementation uses the "SeSamE" strategy: SEarch-and-SAMple planning, then Execution.

Installation

• This repository uses Python versions 3.8+.
• Run pip install -r requirements.txt to install dependencies.

Instructions For Running Code

PYTHONHASHSEED

Our code assumes that python hashing is deterministic between processes, which is not true by default. Please make sure to export PYTHONHASHSEED=0 when running the code. You can add this line to your bash profile, or prepend export PYTHONHASHSEED=0 to any command line call, e.g., export PYTHONHASHSEED=0 python src/main.py --env ....

Locally

• (recommended) Make a new virtual env or conda env.
• Make sure the parent of the repository is on your PYTHONPATH.
• Run, e.g., python src/main.py --env cover --approach oracle --seed 0 to run the system.

Running Experiments on Supercloud

See these instructions.

Running BEHAVIOR Experiments

See these instructions.

Instructions For Contributing

• Run pip install -r requirements-dev.txt to install all dependencies for development.
• 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/predicators.yml, which you can run locally in one line via ./scripts/run_checks.sh, or individually as follows:
  • pytest -s tests/ --cov-config=.coveragerc --cov=src/ --cov=tests/ --cov-fail-under=100 --cov-report=term-missing:skip-covered --durations=0
  • mypy . --config-file mypy.ini
  • pytest . --pylint -m pylint --pylint-rcfile=.predicators_pylintrc
  • yapf -i -r --style .style.yapf . && docformatter -i -r . && isort .
• 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. If it doesn't work due to import errors, try mypy -p predicators --config-file predicators/mypy.ini from one directory up.
• The third one is the linter check, which runs Pylint with the custom config file .predicators_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.