Skip to content

Latest commit

 

History

History
54 lines (35 loc) · 1.85 KB

README.md

File metadata and controls

54 lines (35 loc) · 1.85 KB
Raw

Self-recognition as Optimisation

The repository provides source code for reproducing the experiments described in:

  • Timothy Atkinson and Nihat Engin Toklu, "Self-recognition as Optimisation", Proceedings of ALIFE 2022: The 2022 Conference on Artificial Life. MIT Press, 2022.

Usage

To use this code, the easiest way is to create a conda environment using env.yml:

git clone https://github.com/nnaisense/self-recog-as-optim.git
cd self-recog-as-optim
conda env create -f env.yml

You can then activate the environment:

conda activate selfrec

And run experiments. To run an evolutionary experiment do:

python run/evolution.py [NUM REPEATS] [SEPARATE POLICIES]

where [NUM REPEATS] is the number of repeated evolutionary runs (in the paper: 10) and [SEPARATE POLICIES] is whether to use separate policies (True) or a single policy which must learn to self-recognise (False). Our recommendation is that you do:

python run/evolution.py 10 False
python run/evolution.py 10 True

This will create a folder experiment_logs/ containing two sub-folders: 64_15000_sep and 64_15000_com, each containing 10 evolution runs and corresponding to the separate-policies scenario ('sep') or a single combined policy scenario ('com') respectively.

You can run:

python run/analysis.py experiment_logs/64_15000_com experiment_logs/64_15000_sep 10

to obtain statistical analysis of the experiments and

python run/evolution_playback.py experiment_logs/64_15000_com/0/generation_4000.pkl experiment_logs/64_15000_sep/0/generation_4000.pkl

replacing 0/generation_4000.pkl with any particular evolutionary run you prefer to obtain a replay of learned behaviours and plots such as those shown in Fig. 2.

Maintainers

Developed and maintained by Timothy Atkinson.