This is the code for experiments in the under-reviewed paper "Learning Roles with Emergent Social Value Orientations", which is based on the LIO.
- Python 3.6
- Tensorflow >= 1.12
- OpenAI Gym == 0.10.9
- Clone and
pip install
Sequential Social Dilemma, which is a fork from the original open-source implementation. - Clone this repository and run
$ pip install -e .
from the root.
alg/
- Implementation of RESVO.env/
- Implementation of the Escape Room game and wrappers around the SSD environment.results/
- Results of training will be stored in subfolders here. Each independent training run will create a subfolder that contains the final Tensorflow model, and reward log files. For example, 5 parallel independent training runs would createresults/cleanup/10x10_resvo_0
,...,results/cleanup/10x10_resvo_4
(depending on configurable strings in config files).utils/
- Utility methods.
- Set config values in
alg/config_room_resvo.py
cd
into thealg
folder- Execute training script
$ python train_multiprocess.py resvo er
. Default settings conduct 5 parallel runs with different seeds. - For a single run, execute
$ python train_resvo.py er
.
- Set config values in
alg/config_ssd_resvo.py
cd
into thealg
folder- Execute training script
$ python train_multiprocess.py resvo ssd
. - For a single run, execute
$ python train_ssd.py
.
See LICENSE.
SPDX-License-Identifier: MIT