Interacting with the real world may only be thought off if we understand how to communicate the underlying complex purposes of each systems and how they interact with each other. Reinforcement learning based systems interact with their environments and thus we explore in this article goals of human preferences between several trajectory segments. Indeed we will see that this method can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than
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