We present a multi-agent reinforcement learning framework where rewards are not only generated by the environment but also by other peers in it through inter-agent evaluative feedback. We show that our method allows agents to effectively learn how to use this feedback channel to influence their peers' goals and move from independent or conflicting objectives to more coinciding and concurring ones. We advance with this in the field of cooperative reinforcement learning; not by aiding agents in increasing performance in an already cooperative environment, but by giving them a tool to transform antagonistic environments into cooperative ones.