Within the framework of Multi-Agent Reinforcement Learning, Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games. However, this new modification allows agents unprecedented access to each other's learning process, which can drastically increase the risk of manipulation when an agent does not realize it is being deceived into adopting policies which are not actually in its own best interest. This research review introduces the problem statement, defines key concepts, critically evaluates existing evidence and addresses open problems that should be addressed in future research.

翻译：在多机构强化学习框架内,社会学习是一种新型的算法,使代理人能够重新塑造其他代理人的奖励职能,目的是在混合运动游戏中促进合作和实现更高的全球奖励,然而,这种新的修改允许代理人空前地相互学习,如果代理人没有意识到它被欺骗而采取实际上不符合其自身最佳利益的政策,就会大大增加操纵的风险。本研究审查提出了问题说明,界定了关键概念,严格评估了现有证据,并解决了今后研究中应当解决的公开问题。