Program Synthesis with Reinforcement Learning of Structured Edits

Overview

This project focuses on using reinforcement learning to mutate a partially-correct/complete piece of coding homework to a complete and highly scored (e.g. test inputs all give correct output) homework submission. Our domain uses code written in the functional programming language, OCaml. This project extracts and preprocesses data from a large database of homework submission, transforming them into an abstract syntax tree (AST) and passing the through a graph neural network (GNN).

Build Instructions

To set up via docker, follow the following steps:

1. Install Docker.
2. Set up run-logger.
3. Configure your .env file so that the environment variable GRAPHQL_ENDPOINT is the server you have set up. Start direnv by running direnv allow.

GRAPHQL_ENDPOINT=http://server.com:1200/v1/graphql

4. Create a docker volume called rl_checkpoint by using the command

docker volume create rl_checkpoint

5. Now, you can build the project with docker by running the following commands in the terminal:

bash run.sh <DOCKER_IMAGE_NAME> <DOCKER_VOLUME_MOUNT_DIR> <DESCRIPTION_ON_LOGGER>

Development Instructions

If you want to work on this project on a local machine, you need to install Poetry and opam. You can run make deps to install all dependencies needed.

Visualization Instructions

To visualize the actions that your agent is taking, you can run visualize.sh. This requires you to have saved a model in your docker volume. If you have done so already, run

bash visualize <DOCKER_IMAGE_NAME> <DOCKER_VOLUME_MOUNT_DIR> <LOG_NAME> <RUN_ID>

Code Overview

The following directories each have the following functions:

• agent/: This directory includes the code for our reinforcement learning agent
• clib/: This directory includes the C code for our project. The C code is used for communicating between our Python and OCaml code.
• envs/: This directory includes the Python code for our environment. The environment that we are using is in envs/ast_env.py.
• ocamllib/: This directory includes the OCaml code for our environment.

Bug Fix Notes

1. If there is a sudden error of not finding a child or something like that, check if max_num_nodes is sufficient for problem.