Skip to content

Latest commit

 

History

History
44 lines (32 loc) · 2.34 KB

README.md

File metadata and controls

44 lines (32 loc) · 2.34 KB
Raw

Oracles and Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning

This repository contains code for the ICML 2023 paper "Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning". arXiv version available at https://arxiv.org/abs/2210.11942

Installation

Set up and activate a Python virtual environment using pyenv virtualenv or Anaconda, using python version 3.10.5. Then, run pip install -e . to install our code, as well as all necessary dependencies.

Running Experiments

To run experiments, use the stackerlberg/train/run_experiment.py script. To run each of the experiments in the paper, use the following commands:

# All 12 matrices
python stackerlberg/train/run_experiment.py --experiment=ipd_allmatrices_pg_pg
python stackerlberg/train/run_experiment.py --experiment=ipd_allmatrices_ppo_pg
# Hidden vs observed queries
python stackerlberg/train/run_experiment.py --experiment=smipd_hiddenqueries_pg_pg
python stackerlberg/train/run_experiment.py --experiment=smipd_nothiddenqueries_pg_pg
python stackerlberg/train/run_experiment.py --experiment=smipd_hiddenqueries_dqn_pg
python stackerlberg/train/run_experiment.py --experiment=smipd_nothiddenqueries_dqn_pg
# Invariant leader vs non-invariant
python stackerlberg/train/run_experiment.py --experiment=smipd_leadermemory_pg_pg
python stackerlberg/train/run_experiment.py --experiment=smipd_leadernomemory_pg_pg

You can additionally specify --ray_num_cpus=X to limit the number of CPU cores the program will use, and you can specify --hyperopt to try a variety of learning rates (this was used for the hidden/observed queries and invariant leader experiments). If you set an API key for weights and biases as the environment variable WANDB_API_KEY, the results will be logged to weights and biases.

Specifically for the hidden-queries experiment using DQN, a small change to rllib internal code is necessary, detailed in the next section.

RLlib changes

Inside your Python library folder, in the file ray/rllib/evaluation/postprocessing.py change line 132 and following to

    if not isinstance(rollout[Postprocessing.ADVANTAGES], np.ndarray):
        rollout[Postprocessing.ADVANTAGES] = np.array(
            rollout[Postprocessing.ADVANTAGES].tolist()
        )

This is only needed for the experiment using DQN for the leader.