1 code implementation • 20 Oct 2023 • Joey Hejna, Rafael Rafailov, Harshit Sikchi, Chelsea Finn, Scott Niekum, W. Bradley Knox, Dorsa Sadigh
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.
1 code implementation • 3 Oct 2023 • W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson, Serena Booth, Anca Dragan, Peter Stone, Scott Niekum
Most recent work assumes that human preferences are generated based only upon the reward accrued within those segments, or their partial return.
no code implementations • 5 Jun 2022 • W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro Allievi
We empirically show that our proposed regret preference model outperforms the partial return preference model with finite training data in otherwise the same setting.
no code implementations • 28 Apr 2021 • W. Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, Peter Stone
This article considers the problem of diagnosing certain common errors in reward design.
1 code implementation • 28 Sep 2020 • Yuchen Cui, Qiping Zhang, Alessandro Allievi, Peter Stone, Scott Niekum, W. Bradley Knox
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