Reinforcement Learning from Diverse Human Preferences

arXiv:2301.11774v3 Announce Type: replace
Abstract: The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques. Describing an agent's desired behaviors and properties can be difficult, even for experts. A new paradigm called reinforcement learning from human preferences (or preference-based RL) has emerged as a promising solution, in which reward functions are learned from human preference labels among behavior trajectories. However, existing methods for preference-based RL are limited by the …

abstract agent application arxiv complexity cs.lg designing diverse experts functions human major new paradigm paradigm reinforcement reinforcement learning type