Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models
Robotics
2020-06-05 v1 Artificial Intelligence
Machine Learning
Systems and Control
Abstract
We propose a safe exploration algorithm for deterministic Markov Decision Processes with unknown transition models. Our algorithm guarantees safety by leveraging Lipschitz-continuity to ensure that no unsafe states are visited during exploration. Unlike many other existing techniques, the provided safety guarantee is deterministic. Our algorithm is optimized to reduce the number of actions needed for exploring the safe space. We demonstrate the performance of our algorithm in comparison with baseline methods in simulation on navigation tasks.
Cite
@article{arxiv.1904.01068,
title = {Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models},
author = {Erdem Bıyık and Jonathan Margoliash and Shahrouz Ryan Alimo and Dorsa Sadigh},
journal= {arXiv preprint arXiv:1904.01068},
year = {2020}
}
Comments
Proceedings of the American Control Conference (ACC), July 2019. The first two authors have equal contribution