Search Results for author:
Found 5 papers, 3 papers with code
Contrastive Preference Learning: Learning from Human Feedback without RL
Thus, learning a reward function from feedback is not only based on a flawed assumption of human preference, but also leads to unwieldy optimization challenges that stem from policy gradients or bootstrapping in the RL phase.
Learning Optimal Advantage from Preferences and Mistaking it for Reward
Most recent work assumes that human preferences are generated based only upon the reward accrued within those segments, or their partial return.
Models of human preference for learning reward functions
We empirically show that our proposed regret preference model outperforms the partial return preference model with finite training data in otherwise the same setting.
Reward (Mis)design for Autonomous Driving
This article considers the problem of diagnosing certain common errors in reward design.
The EMPATHIC Framework for Task Learning from Implicit Human Feedback
We train a deep neural network on this data and demonstrate its ability to (1) infer relative reward ranking of events in the training task from prerecorded human facial reactions; (2) improve the policy of an agent in the training task using live human facial reactions; and (3) transfer to a novel domain in which it evaluates robot manipulation trajectories.
Human-Computer Interaction Robotics
