Individual rationality, which involves maximizing expected individual returns, does not always lead to high-utility individual or group outcomes in multi-agent problems. For instance, in multi-agent social dilemmas, Reinforcement Learning (RL) agents trained to maximize individual rewards converge to a low-utility mutually harmful equilibrium. In contrast, humans evolve useful strategies in such social dilemmas. Inspired by ideas from human psychology that attribute this behavior to the status-quo bias, we present a status-quo loss (SQLoss) and the corresponding policy gradient algorithm that incorporates this bias in an RL agent. We demonstrate that agents trained with SQLoss learn high-utility policies in several social dilemma matrix games (Prisoner's Dilemma, Stag Hunt matrix variant, Chicken Game). We show how SQLoss outperforms existing state-of-the-art methods to obtain high-utility policies in visual input non-matrix games (Coin Game and Stag Hunt visual input variant) using pre-trained cooperation and defection oracles. Finally, we show that SQLoss extends to a 4-agent setting by demonstrating the emergence of cooperative behavior in the popular Braess' paradox.
@article{arxiv.2111.11692,
title = {Status-quo policy gradient in Multi-Agent Reinforcement Learning},
author = {Pinkesh Badjatiya and Mausoom Sarkar and Nikaash Puri and Jayakumar Subramanian and Abhishek Sinha and Siddharth Singh and Balaji Krishnamurthy},
journal= {arXiv preprint arXiv:2111.11692},
year = {2021}
}