This is the code for experiments in the paper IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social Dilemmas. This implementation benefits from LIO and JAXRL.

• Python 3.8
• Tensorflow 2.2.1
• Flax 0.3.4
• Jax 0.2.17
• Jaxlib 0.3.0
• Gym 0.26.2
• Follow the setup instructions for the official repository of the paper Learning to Incentivize Other Learning Agents at LIO.
• Run $ pip install -e . from the root.
• In order to do hyperparameter tuning, install Optuna.

• alg/ - Implementation of IQ-Flow and baselines.
• config/ - Configuration files for experiments.
• env/ - Implementation of the Escape Room game and wrappers around the SSD environment from LIO and Iterated Matrix Games.
• eval/ - Evaluation scripts
• networks/ - Neural network implementations
• trainer/ - Training scripts
• utils/ - Utilities

• Set config values in config/config_img_qflow.py
• cd into the trainer folder
• Execute training script $ python train_multiprocess.py --alg qflow --exp ipd --n_seeds 5 --config config_img_qflow to run Iterated Prisoner's Dilemma with 5 seeds. You can see the argument options for further configuration details.
• Execute training script $ python train_multiprocess.py --alg qflow --exp chicken --n_seeds 5 --config config_img_qflow to run Chicken Game with 5 seeds.
• Execute training script $ python train_multiprocess.py --alg qflow --exp stag_hunt --n_seeds 5 --config config_img_qflow to run Stag Hunt with 5 seeds.

• Set config values in config/config_er_qflow.py
• cd into the trainer folder
• Execute training script $ python train_multiprocess.py --alg qflow --exp er --n_seeds 5 --config config_er_qflow to run Escape with 5 seeds. You can see the argument options for further configuration details.

• Set config values in config/config_ssd_qflow.py
• cd into the trainer folder
• Execute training script $ python train_multiprocess.py --alg qflow --exp ssd --n_seeds 5 --config config_ssd_qflow to run Cleanup with 5 seeds. You can see the argument options for further configuration details.

• Set config values in config/config_er_id.py
• cd into the trainer folder
• Execute training script $ python train_multiprocess.py --alg id --exp er --n_seeds 5 --config config_er_id to run Escape with 5 seeds. You can see the argument options for further configuration details.

• Set config values in config/config_ssd_id.py
• cd into the trainer folder
• Execute training script $ python train_multiprocess.py --alg id --exp ssd --n_seeds 5 --config config_ssd_id to run Cleanup with 5 seeds. You can see the argument options for further configuration details.