This literature review aims to thoroughly examine the current landscape of Safe Reinforcement Learning techniques for autonomous vehicles. The paper aims to cover various topics around safe RL, including value function approximation, policy optimization, model-based reinforcement learning, safe exploration te...
这篇文章将为大家解析由慕尼黑工业大学、同济大学、加州大学伯克利分校、伦敦大学学院、伦敦国王大学和北京大学的研究人员联合发布的综述《安全强化学习:方法、理论与应用》的重要观点,深入探讨安全强化学习的研究现状、关键问题及未来发展方向。 论文标题:A Review of Safe Reinforcement Learning: Methods, Theories and Ap...
We conduct a comprehensive review of the existing literature on safe reinforcement learning using control barrier functions. Additionally, we investigate various... M Guerrier,H Fouad,G Beltrame 被引量: 0发表: 2024年 Safety of Autonomous Systems Using Reinforcement Learning: A Comprehensive Survey The...
This paper delves into the problem of safe reinforcement learning (RL) in a partially observable environment with the aim of achieving safe-reachability objectives. In traditional partially observable Markov decision processes (POMDP), ensuring safety typically involves estimating the belief in latent stat...
Safe-Reinforcement-Learning-Baselines The repository is for Safe Reinforcement Learning (RL) research, in which we investigate various safe RL baselines and safe RL benchmarks, including single agent RL and multi-agent RL. If any authors do not want their paper to be listed here, please feel ...
内容提示: Dynamic Model Predictive Shielding forProvably Safe Reinforcement LearningArko BanerjeeThe University of Texas at Austinarko.banerjee@utexas.eduKia RahmaniThe University of Texas at Austinkiar@utexas.eduJoydeep BiswasThe University of Texas at Austinjoydeepb@utexas.eduIsil DilligThe University ...
Multi-criteria Reinforcement Learning, Paper, Not Find Code, (Accepted by ICML 1998) Lyapunov design for safe reinforcement learning, Paper, Not Find Code, (Accepted by ICML 2002) Risk-sensitive reinforcement learning, Paper, Not Find Code, (Accepted by Machine Learning, 2002) Risk-Sensitive Rein...
A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline ...
Safe learning in robotics: From learning-based control to safe reinforcement learning[J]. Annual Review of Control, Robotics, and Autonomous Systems, 2022, 5: 411-444. [2] Ames A D, Xu X, Grizzle J W, et al. Control barrier function based quadratic programs for safety critical systems[J...
Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning In the real world, agents often have to operate in situations with incomplete information, limited sensing capabilities, and inherently stochastic environm... A Agarwal,KV Abhinau,K Dunovan,... 被引量: 0发表: 201...