LOQA: Learning with Opponent Q-Learning Awareness
This addresses the challenge of finding equilibria that balance individual and social welfare in multi-agent systems, representing an incremental improvement for practical applications.
The paper tackles the problem of decentralized learning in general-sum games by introducing LOQA, a reinforcement learning algorithm that optimizes individual utility while promoting cooperation, achieving state-of-the-art performance in benchmarks like the Iterated Prisoner's Dilemma and Coin Game with reduced computational costs.
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.