  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.
