6 citations · 8 across the 5 of their papers we have counts for
5 papers
Trajectory Improvement and Reward Learning from Comparative Language Feedback
Zhaojing Yang, Miru Jun, Jeremy Tien +3
Learning from human feedback has gained traction in fields like robotics and natural language processing in recent years. While prior works mostly rely on human feedback in the for…
AI Alignment with Changing and Influenceable Reward Functions
Micah Carroll, Davis Foote, Anand Siththaranjan +2
Existing AI alignment approaches assume that preferences are static, which is unrealistic: our preferences change, and may even be influenced by our interactions with AI systems th…
A Generalized Acquisition Function for Preference-based Reward Learning
Evan Ellis, Gaurav R. Ghosal, Stuart J. Russell +2
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that act…
When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback
Leon Lang, Davis Foote, Stuart Russell +3
Past analyses of reinforcement learning from human feedback (RLHF) assume that the human evaluators fully observe the environment. What happens when human feedback is based only on…
The Effective Horizon Explains Deep RL Performance in Stochastic Environments
Cassidy Laidlaw, Banghua Zhu, Stuart Russell +1
Reinforcement learning (RL) theory has largely focused on proving minimax sample complexity bounds. These require strategic exploration algorithms that use relatively limited funct…