Evolution of reward functions for reinforcement learning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Emotion-based intrinsic motivation for reinforcement learning agents
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Expressive genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Reward functions in reinforcement learning have largely been assumed given as part of the problem being solved by the agent. However, the psychological notion of intrinsic motivation has recently inspired inquiry into whether there exist alternate reward functions that enable an agent to learn a task more easily than the natural task-based reward function allows. This paper presents a genetic programming algorithm to search for alternate reward functions that improve agent learning performance. We present experiments that show the superiority of these reward functions, demonstrate the possible scalability of our method, and define three classes of problems where reward function search might be particularly useful: distributions of environments, nonstationary environments, and problems with short agent lifetimes.