Results 21 to 30 of about 7,404,725 (288)
Safe and Efficient Operation with Constrained Hierarchical Reinforcement Learning
, 2023 Hierarchical Reinforcement Learning (HRL) holds the promise of enhancing sample efficiency and generalization capabilities of Reinforcement Learning (RL) agents by leveraging task decomposition and temporal abstraction, which aligns with human reasoning.
Günnemann, Stephan +2 more
core +1 more source
Safe Reinforcement Learning on Autonomous Vehicles [PDF]
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safety critical applications. Recent work in safe reinforcement learning uses idealized models to achieve their guarantees, but these models do not easily accommodate the ...
David Isele +2 more
openaire +3 more sources
Stability-Certified Reinforcement Learning: A Control-Theoretic Perspective
IEEE Access, 2020 We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems. We show that by regulating the partial gradients of policies, strong guarantees of robust stability can ...
Ming Jin, Javad Lavaei
doaj +1 more source
Safe Reinforcement Learning for Legged Locomotion
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022 Designing control policies for legged locomotion is complex due to the under-actuated and non-continuous robot dynamics. Model-free reinforcement learning provides promising tools to tackle this challenge. However, a major bottleneck of applying model-free reinforcement learning in real world is safety.
Tsung-Yen Yang +5 more
openaire +3 more sources
GUARD: A Safe Reinforcement Learning Benchmark
Trans. Mach. Learn. Res., 2023 Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-critical real-world applications, such as autonomous driving, human-robot interaction, robot manipulation, etc, where such errors are not tolerable. Recently, safe RL (i.e.
Weiye Zhao +6 more
openaire +4 more sources
Autonomous Intersection Management by Using Reinforcement Learning
Algorithms, 2022 Developing a safer and more effective intersection-control system is essential given the trends of rising populations and vehicle numbers. Additionally, as vehicle communication and self-driving technologies evolve, we may create a more intelligent ...
P. Karthikeyan +2 more
doaj +1 more source
What Is Acceptably Safe for Reinforcement Learning? [PDF]
, 2018 Machine Learning algorithms are becoming more prevalent in critical systems where dynamic decision making and efficiency are the goal. As is the case for complex and safety-critical systems, where certain failures can lead to harm, we must proactively consider the safety assurance of such systems that use Machine Learning.
Bragg, John Edward, Habli, Ibrahim
openaire +2 more sources
Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones [PDF]
IEEE Robotics and Automation Letters, 2021 RA-L and ICRA 2021.
Brijen Thananjeyan +9 more
openaire +4 more sources
Safe Reinforcement Learning for Sepsis Treatment
2020 IEEE International Conference on Healthcare Informatics (ICHI), 2020 Sepsis, a life-threatening illness, is estimated to be the primary cause of death for 50,000 people a year in the UK and many more worldwide. Managing the treatment of sepsis is very challenging as it is frequently missed at an early stage and the optimal treatment is not yet clear.
Jia, Yan +3 more
openaire +3 more sources
A human-centered safe robot reinforcement learning framework with interactive behaviors
Frontiers in Neurorobotics, 2023 Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is a crucial step toward achieving human-robot coexistence. In this paper,
Shangding Gu +5 more
doaj +1 more source