Results 261 to 270 of about 7,404,725 (288)

Multi-objective safe reinforcement learning: the relationship between multi-objective reinforcement learning and safe reinforcement learning

Artificial Life and Robotics, 2019
Reinforcement learning (RL) is a learning method that learns actions based on trial and error. Recently, multi-objective reinforcement learning (MORL) and safe reinforcement learning (SafeRL) have been studied. The objective of conventional RL is to maximize the expected rewards; however, this may cause a fatal state because safety is not considered ...
Naoto Horie, Tohgoroh Matsui, Koichi Moriyama, Atsuko Mutoh, Nobuhiro Inuzuka   +4 more
openaire   +1 more source

Safe Online and Offline Reinforcement Learning

2022
Algorithmics
openaire   +3 more sources

Lyapunov design for safe reinforcement learning

J. Mach. Learn. Res., 2002
Summary: Lyapunov design methods are used widely in control engineering to design controllers that achieve qualitative objectives, such as stabilizing a system or maintaining a system's state in a desired operating range. We propose a method for constructing safe, reliable reinforcement learning agents based on Lyapunov design principles.
Perkins, TJ, Barto, AG
openaire   +2 more sources

Safe Reinforcement Learning

2015
Test scores are usually equated only at the total score level. If a test mainly measures a single trait, indicating that the test is essentially unidimensional, equating at the total score level could be the best choice. However, when a test is composed of subtests having negligible relationships among them, separate equating for each subtest offers ...
openaire   +1 more source

A comprehensive survey on safe reinforcement learning

J. Mach. Learn. Res., 2015
Summary: Safe Reinforcement Learning can be defined as the process of learning policies that maximize the expectation of the return in problems in which it is important to ensure reasonable system performance and/or respect safety constraints during the learning and/or deployment processes. We categorize and analyze two approaches of Safe Reinforcement
Javier García 0001, Fernando Fernández 0001   +1 more
openaire   +2 more sources

On Normative Reinforcement Learning via Safe Reinforcement Learning

2022
Emery A. Neufeld, Ezio Bartocci, Agata Ciabattoni   +2 more
openaire   +2 more sources

Data Efficient Safe Reinforcement Learning

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022
Sindhu Padakandla, Prabuchandran K. J., Sourav Ganguly, Shalabh Bhatnagar   +3 more
openaire   +2 more sources

Learning a Low-Dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems

IEEE Transactions on Neural Networks and Learning Systems, 2023
Salih Özgür Ögüz, Zhehua Zhou, Martin Buss   +2 more
exaly  

Safe Reinforcement Learning With Stability Guarantee for Motion Planning of Autonomous Vehicles

IEEE Transactions on Neural Networks and Learning Systems, 2021
Ye Zhao, Minghao Han, Lixian Zhang
exaly  

Flooding mitigation through safe & trustworthy reinforcement learning

Journal of Hydrology, 2023
Kunlun Xin, Zhenliang Liao, Muhan Zhao
exaly