https://github.com/igorpejic/visapi
Monte Carlo Tree Search on Perfect Rectangle Packing Problem Instances
https://github.com/igorpejic/visapi
bin-packing binpack monte-carlo-tree-search rectangle-packing rectanglepacking
Last synced: 3 months ago
JSON representation
Monte Carlo Tree Search on Perfect Rectangle Packing Problem Instances
- Host: GitHub
- URL: https://github.com/igorpejic/visapi
- Owner: igorpejic
- Created: 2019-11-27T18:51:07.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T04:22:42.000Z (almost 3 years ago)
- Last Synced: 2024-04-16T07:06:35.873Z (over 1 year ago)
- Topics: bin-packing, binpack, monte-carlo-tree-search, rectangle-packing, rectanglepacking
- Language: Jupyter Notebook
- Homepage:
- Size: 9.31 MB
- Stars: 9
- Watchers: 3
- Forks: 2
- Open Issues: 49
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Monte Carlo Tree Search on Perfect Rectangle Packing Problem Instances
## Dataset
The dataset of 1000 Guillotinable and 1000 Braam rectangle packing problems with 20 tiles can be found in [problems](problems/).
## Installation
```
pip3 install requirements.txt
```
To run experiments, you will need to have a PostgreSQL database running.
After installing the PostgreSQL database you can create one by logging into the psql shell and running:
```psql
postgres=# create role binpack_user;
CREATE ROLE
postgres=# create database binpack;
CREATE DATABASE
postgres=# grant all on database binpack to binpack_user;
GRANT
postgres=# alter role binpack_user with login;
postgres=# alter user binpack_user with password 'password';
```
Copy the file `visapi/secrets.json.local` to `visapi/secrets.json`:
```
cp visapi/secrets.json.template visaip/secrets.json
```
Then edit it with a secret key and the details to connect to the database:
```
The database values meanings are in order:
HOST, DATABASE_NAME, USER, PASSWORD, PORT
```
For the example provided, the contents of the file would be:
```json
{
"SECRET_KEY": "something_random",
"DATABASE": ["localhost", "binpack", "binpack_user", "password", ""]
}
```
To create a database schema, you will need to run:
```shell
python3 manage.py migrate
```
All experiments results will be saved to the database, in the table `affinity`.
Access them using the django shell:
```
python3 manage.py shell_plus
```
And then:
```python
Result.objects.all()
```
See Django ORM [reference](https://docs.djangoproject.com/en/3.1/topics/db/queries/#making-queries).
Or access them using postgresql shell:
```psql
select * from affinity;
```
All the problem results will be available in the database with all the metrics needed to finish them (number of tiles placed etc.), and their scores (success / failure).
## Running experiments
### MCTS algorithm
Run:
```
PYTHONPATH=engine/ python3 manage.py run_mcts
```
to see all the options for the runner.
Example:
Run dynamically generated problems:
```
PYTHONPATH=engine/ python3 manage.py run_mcts 20 11 11
```
From file:
```
PYTHONPATH=engine/ python3 manage.py run_mcts 20 11 11 --from_file
```
### Neural Network algorithm
This algorithm performs only one tile placement.
To get the final solution calls the NN predict multiple times.
To train the neural network you can use:
```
PYTHONPATH=engine/ python3 manage.py just_train
```