@inproceedings{xie2021interaction-grounded,
author = {Xie, Tengyang and Langford, John and Mineiro, Paul and Momennejad, Ida},
title = {Interaction-Grounded Learning},
booktitle = {2021 International Conference on Machine Learning},
year = {2021},
month = {July},
abstract = {Consider a prosthetic arm, learning to adapt to its user's control signals. We propose Interaction-Grounded Learning for this novel setting, in which a learner's goal is to interact with the environment with no grounding or explicit reward to optimize its policies. Such a problem evades common RL solutions which require an explicit reward. The learning agent observes a multidimensional context vector, takes an action, and then observes a multidimensional feedback vector. This multidimensional feedback vector has no explicit reward information. In order to succeed, the algorithm must learn how to evaluate the feedback vector to discover a latent reward signal, with which it can ground its policies without supervision. We show that in an Interaction-Grounded Learning setting, with certain natural assumptions, a learner can discover the latent reward and ground its policy for successful interaction. We provide theoretical guarantees and a proof-of-concept empirical evaluation to demonstrate the effectiveness of our proposed approach.},
url = {http://approjects.co.za/?big=en-us/research/publication/interaction-grounded-learning/},
}