The Importance of Credo in Multiagent Learning

The recent We propose a model for multi-objective optimization, a credo, for call to make cooperation central to the development of AI places emphasis agents in a system that are configured into multiple groups (i.e., on understanding the mechanisms behind teamwork beyond teams). Our model of credo regulates how agents optimize their just competition [14, 15] and to adapt findings from Organizational behavior for the groups they belong to. We evaluate credo in the Psychology [5]. In MARL, agents learning to cooperate often build context of challenging social dilemmas with reinforcement learning common interest by sharing exogenous rewards [1, 7]; however, agents. Our results indicate that the interests of teammates, or the purely pro-social agents may not be possible when considering entire system, are not required to be fully aligned for achieving agents designed by different manufacturers or hybrid AI/human globally beneficial outcomes. We identify two scenarios without populations. Agents in these settings may have some self-interest full common interest that achieve high equality and significantly for personal goals; therefore, it is important to understand how and higher mean population rewards compared to when the interests when cooperation can be supported in systems where agents may of all agents are aligned.