LOQA: Learning with Opponent Q-Learning Awareness

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-theart 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. A major difficulty in reinforcement learning (RL) and multi-agent reinforcement learning (MARL) is the non-stationary nature of the environment, where the outcome of each agent is determined not only by their own actions but also those of other players von Neumann (1928). This difficulty often results in the failure of traditional algorithms converging to desirable solutions. In the context of general-sum games, independent RL agents often converge to sub-optimal solutions in the Pareto sense, when each of them seeks to optimize their own utility Foerster et al. (2018b).