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 create results/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 the alg 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 the alg 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