Emotionally Unbiased Reinforcement Learning (EURL) introduces a novel approach to managing equilibrium in multi-agent systems where agents face inherently conflicting objectives. Unlike traditional multi-agent reinforcement learning (MARL) frameworks that prioritize joint reward maximization, EURL minimizes conflicts through an impartial, emotionally neutral decision-making strategy, enabling balanced outcomes even when agent goals are at odds. The framework’s innovative design integrates fairness and conflict reduction as primary optimization goals that yield stability in equitable interactions without enforced cooperation. Extensive simulations of autonomous traffic control, collaborative robotics, and negotiation systems indicate a significant potential for EURL in reducing the rate of conflicts and enhancing stability. By setting a baseline for emotionally neutral AI, EURL opens the door to more powerful multi-agent conflict management and thus has immediate applicability to domains such as fair and unbiased decision-making and resource allocation.