Code for a pytorch implementation of the mutation operators from the paper.
This repository contains code for the illustrative experiments, the recurrent parity task, and the breadcrumb hard maze task.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- PyTorch -- version 0.2.0_2 was what was used in the experiment
- Python -- version 2.7
- PyGame -- only relevant for visualization
- SCons -- to build the hard maze domain
If you want to run the breadcrumb hard maze experiment, you will need to install SCons and run the command scons in the working directory, which should build from source the python module. Note that the current release requires python 2.7 (although it is likely possible to port it to python 3).
sm_simple.py contains code to recreate the illustrative experiments, and can be invoked in the
following way:
python sm_simple.py --domain [domain] --mutation [method] --mutation_mag [float]
The parameter domain can be one of: easy, medium, or washout (for the gradient-washout task).
The parameter mutation can be one of: control, SM-G-SUM, SM-G-SO, SM-G-ABS, or SM-R.
The parameter mutation_mag can be set to an arbitrary float value. Parameter values used in the paper's experiments can be found in the appendix.
experiment_runner.py contains code to run experiments in the recurrent parity task and the breadcrumb hard maze. It can be invoked in the following way:
``python experiment_runner.py --domain [domain] --mutation [method] --mutation_mag [float]```
The parameter domain can be one of: classification (for the recurrent parity task) or breadcrumb_maze (for the breadcrumb_hard_maze).
The parameter mutation can be one of: control, SM-G-SUM, SM-G-SO, SM-G-ABS, or SM-R.
The parameter mutation_mag can be set to an arbitrary float value. Parameter values used in the paper's experiments can be found in the appendix.
This project is licensed under the MIT License - see the LICENSE.md file for details