The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provided by each interaction is not retained and instead discarded by the agent after a single use. In this paper, we present a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Results obtained show that the use of broad-persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer.
@article{arxiv.2210.05187,
title = {Broad-persistent Advice for Interactive Reinforcement Learning Scenarios},
author = {Francisco Cruz and Adam Bignold and Hung Son Nguyen and Richard Dazeley and Peter Vamplew},
journal= {arXiv preprint arXiv:2210.05187},
year = {2022}
}
Comments
Extended abstract accepted at the 2nd RL-CONFORM Workshop at IEEE/RSJ IROS'22 Conference. 5 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:2102.02441, arXiv:2110.08003