Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking

13 May 2022

Papers citing "Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking"

6 / 6 papers shown

Title
Defining and Characterizing Reward Hacking Joar Skalse Nikolaus H. R. Howe Dmitrii Krasheninnikov David M. Krueger 59 55 0 27 Sep 2022
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments Jakob Thumm Matthias Althoff 52 34 0 12 May 2022
Runtime Safety Assurance for Learning-enabled Control of Autonomous Driving Vehicles Shengduo Chen Yao Sun Dachuan Li Qiang Wang Qi Hao J. Sifakis 57 15 0 28 Sep 2021
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic Mingyu Cai Mohammadhosein Hasanbeig Shaoping Xiao Alessandro Abate Z. Kan 80 86 0 24 Feb 2021
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine Aviral Kumar George Tucker Justin Fu OffRL GP 340 1,960 0 04 May 2020
Deep Reinforcement Learning for Autonomous Driving: A Survey B. R. Kiran Ibrahim Sobh V. Talpaert Patrick Mannion A. A. Sallab S. Yogamani P. Pérez 165 1,632 0 02 Feb 2020