4 papers
Human-Inspired Multi-Level Reinforcement Learning
Mingkang Wu, Devin White, Vernon Lawhern +2
Reinforcement learning (RL), a common tool in decision making, learns control policies from various experiences based on the associated cumulative return/rewards without treating t…
Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose +3
Reinforcement learning from human feedback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when mak…
Crowd-PrefRL: Preference-Based Reward Learning from Crowds
David Chhan, Ellen Novoseller, Vernon J. Lawhern
Preference-based reinforcement learning (RL) provides a framework to train AI agents using human feedback through preferences over pairs of behaviors, enabling agents to learn desi…
Performance Optimization of Ratings-Based Reinforcement Learning
Evelyn Rose, Devin White, Mingkang Wu +3
This paper explores multiple optimization methods to improve the performance of rating-based reinforcement learning (RbRL). RbRL, a method based on the idea of human ratings, has b…