  In various real-world scenarios, interactions among agents often resemble the
dynamics of general-sum games, where each agent strives to optimize its own
utility. Despite the ubiquitous relevance of such settings, decentralized
machine learning algorithms have struggled to find equilibria that maximize
individual utility while preserving social welfare. In this paper we introduce
Learning with Opponent Q-Learning Awareness (LOQA), a novel, decentralized
reinforcement learning algorithm tailored to optimizing an agent's individual
utility while fostering cooperation among adversaries in partially competitive
environments. LOQA assumes the opponent samples actions proportionally to their
action-value function Q. Experimental results demonstrate the effectiveness of
LOQA at achieving state-of-the-art performance in benchmark scenarios such as
the Iterated Prisoner's Dilemma and the Coin Game. LOQA achieves these outcomes
with a significantly reduced computational footprint, making it a promising
approach for practical multi-agent applications.
