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Solving a reinforcement learning problem typically involves correctlyprespecifying the reward signal from which the algorithm learns. Here, weapproach the problem of reward signal design by using an evolutionary approachto perform a search on the space of all possible reward signals. We introduce ageneral framework for optimizing $N$ goals given $n$ reward signals. Throughexperiments we demonstrate that such an approach allows agents to learnhigh-level goals - such as winning, losing and cooperating - from scratchwithout prespecified reward signals in the game of Pong. Some of the solutionsfound by the algorithm are surprising, in the sense that they would probablynot have been chosen by a person trying to hand-code a given behaviour througha specific reward signal. Furthermore, it seems that the proposed approach mayalso benefit from higher stability of the training performance when comparedwith the typical score-based reward signals.

Learning to Win, Lose and Cooperate through Reward Signal Evolution