SRSD uses human-provided semantic labels to learn rewards that encourage reinforcement learning agents to discover a wide variety of meaningful and distinct behaviors.
In: Proceedings of the 40th International Conference on Ma- chine Learning
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Leveraging Human Feedback for Semantically-Relevant Skill Discovery
SRSD uses human-provided semantic labels to learn rewards that encourage reinforcement learning agents to discover a wide variety of meaningful and distinct behaviors.