Active Inverse Reward Design

9 Sep 2018Sören MindermannRohin ShahAdam GleaveDylan Hadfield-Menell

Designers of AI agents often iterate on the reward function in a trial-and-error process until they get the desired behavior, but this only guarantees good behavior in the training environment. We propose structuring this process as a series of queries asking the user to compare between different reward functions... (read more)

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