no code implementations • 19 Sep 2022 • Aaron M. Roth, Jing Liang, Ram Sriram, Elham Tabassi, Dinesh Manocha
Moreover, we present efficient policy distillation and tree-modification techniques that take advantage of the decision tree structure to allow improvements to a policy without retraining.
1 code implementation • 22 Apr 2021 • Aaron M. Roth, Jing Liang, Dinesh Manocha
In order to increase the reliability and handle the failure cases of the expert policy, we combine with a policy extraction technique to transform the resulting policy into a decision tree format.
1 code implementation • 2 Jul 2019 • Aaron M. Roth, Nicholay Topin, Pooyan Jamshidi, Manuela Veloso
There is a growing desire in the field of reinforcement learning (and machine learning in general) to move from black-box models toward more "interpretable AI."
1 code implementation • 10 Jun 2018 • Aaron M. Roth, Umang Bhatt, Tamara Amin, Afsaneh Doryab, Fei Fang, Manuela Veloso
In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human's decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting.