This repo is an open-source implementation of DeepMind's Sequential Social Dilemma (SSD) multi-agent game-theoretic environments [1]. SSDs can be thought of as analogous to spatially and temporally extended Prisoner's Dilemma-like games. The reward structure poses a dilemma because individual short-term optimal strategies lead to poor long-term outcomes for the group.
The implemented environments are structured to be compatible with OpenAIs gym environments (https://github.com/openai/gym) as well as RLlib's Multiagent Environment (https://github.com/ray-project/ray/blob/master/python/ray/rllib/env/multi_agent_env.py)
- Cleanup: A public goods dilemma in which agents get a reward for consuming apples, but must use a cleaning beam to clean a river in order for apples to grow. While an agent is cleaning the river, other agents can exploit it by consuming the apples that appear.
- Harvest: A tragedy-of-the-commons dilemma in which apples regrow at a rate that depends on the amount of nearby apples. If individual agents employ an exploitative strategy by greedily consuming too many apples, the collective reward of all agents is reduced.
The above plot shows the empirical Schelling diagrams for both Cleanup (A) and Harvest (B) (from [2]). These diagrams show the payoff that an individual agent can expect if it follows a defecting/exploitative strategy (red) vs a cooperative strategy (blue), given the number of other agents that are cooperating. We can see that an individual agent can almost always greedily benefit from detecting, but the more agents that defect, the worse the outcomes for all agents.
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Leibo, J. Z., Zambaldi, V., Lanctot, M., Marecki, J., & Graepel, T. (2017). Multi-agent reinforcement learning in sequential social dilemmas. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (pp. 464-473).
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Hughes, E., Leibo, J. Z., Phillips, M., Tuyls, K., Dueñez-Guzman, E., Castañeda, A. G., Dunning, I., Zhu, T., McKee, K., Koster, R., Tina Zhu, Roff, H., Graepel, T. (2018). Inequity aversion improves cooperation in intertemporal social dilemmas. In Advances in Neural Information Processing Systems (pp. 3330-3340).
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Jaques, N., Lazaridou, A., Hughes, E., Gulcehre, C., Ortega, P. A., Strouse, D. J., Leibo, J. Z. & de Freitas, N. (2018). Intrinsic Social Motivation via Causal Influence in Multi-Agent RL. arXiv preprint arXiv:1810.08647.
- Create
causalvirtual environment:conda env create -n causal environment.yml - Run
python setup.py develop - Activate your environment by running
source activate causal, orconda activate causal.
To then set up the branch of Ray on which we have built the causal influence code, clone the repo to your desired folder:
git clone https://github.com/natashamjaques/ray.git.
Next, go to the rllib folder:
cd ray/python/ray/rllib and run the script python setup-rllib-dev.py. This will copy the rllib folder into the pip install of Ray and allow you to use the version of RLlib that is in your local folder by creating a softlink.
Tests are located in the test folder and can be run individually or run by running python -m pytest. Many of the less obviously defined rules for the games can be understood by reading the tests, each of which outline some aspect of the game.
Every environment that subclasses MapEnv probably needs to implement the following methods
def custom_reset(self):
"""Reset custom elements of the map. For example, spawn apples"""
pass
def custom_action(self, agent, action):
"""Execute any custom, non-move actions that may be defined, like fire or clean"""
pass
def custom_map_update(self):
"""Custom map updates that don't have to do with agent actions"""
pass
def setup_agents(self):
"""Construct all the agents for the environment"""
raise NotImplementedError
This code base was developed by Eugene Vinitsky and Natasha Jaques; help with reproduction was provided by Joel Leibo, Antonio Castenada, and Edward Hughes.