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.
[
add
]
[
|
|
...
]