Repository for the Capstone Project 3D Packing Optimization of the Fourthbrain Machine Learning Engineer program.

This repository contains an environment compatible with OpenAI Gym's API to solve the 3D bin packing problem with reinforcement learning (RL).

The environment consists of a list of 3D boxes of varying sizes and a single container of fixed size. The goal is to pack as many boxes as possible in the container minimizing the empty volume. We assume that rotation of the boxes is not possible.

The function boxes_generator in the file utils.py generates instances of the 3D Bin Packing problem using the algorithm described in Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization (Algorithm 2, Appendix).

The documentation for this project is located in the doc folder, with a complete description of the state and action space as well as the rewards to be used for RL training.

We recommend that you create a virtual environment with Python 3.8 (for example, using conda environments). In your terminal window, activate your environment and clone the repository:

git clone https://github.com/luisgarciar/3D-bin-packing.git

To run the code, you need to install a few dependencies. Go to the cloned directory and install the required packages:

cd 3D-bin-packing
pip install -r requirements.txt

The module packing_engine (located in src/packing_engine.py) implements the Container and Box objects that are used in the Gym environment. To add custom features (for example, to allow rotations), see the documentation of this module.

The Gym environment is implemented in the module src/packing_env.py.

A demo notebook demo_ffd implementing the heuristic-based method 'First Fit Decreasing' is available in the nb folder.

The folder tests contains unit tests to be run with pytest.

The following updates have been made to the repository:

• Added the packing_env.py file with the Gym environment.
• Added unit tests for the Gym environment.
• Updated the documentation with the full description of the state and action space.
• Updated the demo notebooks.

The following updates have been made to the repository:

• Added functionality for saving rollouts of a policy in a .gif file and
• Added a demo notebook for the random policy.
• Updated the requirements.txt file with the required packages.
• Added a demo script for training agents with Maskable PPO.

The following updates have been made to the repository:

• Updated the demo notebook for training agents with Maskable PPO in Google colab.
• Fixed issues with the tests.