One of the most time-consuming and challenging parts in designing a robotic agent today is engineering the reward function. Our work proposes a way to scale robot learning by crowdsourcing the design of reward functions.
Our work proposes a way to scale robot learning by crowdsourcing the design of reward functions and by making it possible for nonexperts to provide useful feedback.
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