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While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present andoi:10.1109/roman.2006.314459 dblp:conf/ro-man/ThomazHB06 fatcat:hmssozyf7rdk7ghuzzi6vaiu34