👉 Rank engineering is a strategy used in machine learning, particularly in reinforcement learning and multi-agent systems, to improve the performance of agents by modifying their policy or value functions to better reflect their rank in a competitive environment. It involves adjusting the agent's behavior to not only optimize for individual rewards but also to consider its relative standing against other agents. This can be achieved through various techniques, such as adding a rank-based penalty to the loss function, modifying the reward structure, or employing specialized algorithms that directly account for the agent's position within a multi-agent system. By doing so, rank engineering helps agents adapt more effectively to dynamic environments where competition or cooperation with other agents is a factor, ultimately leading to more robust and efficient learning processes.
