229 citations · 597 across the 19 of their papers we have counts for
3 papers · 1 filter
LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback
Manith Adikari, Bei Peng, Samuele Vinanzi +1
Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function. However, real-world decision-making tasks often involve multiple, c…
Interactive Learning of Environment Dynamics for Sequential Tasks
Robert Loftin, Bei Peng, Matthew E. Taylor +2
In order for robots and other artificial agents to efficiently learn to perform useful tasks defined by an end user, they must understand not only the goals of those tasks, but als…
Interactive Learning from Policy-Dependent Human Feedback
James MacGlashan, Mark K Ho, Robert Loftin +5
This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has…