This paper introduces Advantage Alignment, a new family of algorithms designed to enhance the ability of artificial intelligence agents to navigate social dilemmas, situations where individual optimization leads to suboptimal collective outcomes. The research demonstrates that existing opponent shaping methods, like LOLA and LOQA, implicitly use Advantage Alignment. By aligning the "advantages" (benefits beyond the expected outcome) of competing agents and increasing the probability of mutually beneficial actions, Advantage Alignment offers a simplified mathematical framework for opponent shaping. The effectiveness of this approach is shown through experiments in classic social dilemmas such as the Iterated Prisoner's Dilemma and the Coin Game, achieving state-of-the-art results in a variation of the Negotiation Game, highlighting its potential for real-world applications like climate negotiation strategies.