今天先以一篇综述《State-wise Safe Reinforcement Learning: A Survey》 作为开端,文献地址为: [2302.03122] State-wise Safe Reinforcement Learning: A Survey 上述综述主要是围绕一个概念:基于状态的安全强化学习展开的。我个人感觉讲的还是比较清楚的,尤其是针对目前比较火热的“基于状态约束”的一大类问题(比如说...
最后介绍一个测试Safe RL算法的环境。在19年OpenAI设计了一系列用于评估Safe RL的环境,称为safety-gym,文章叫:Benchmarking Safe Exploration in Deep Reinforcement Learning,这环境基于Mujoco开发的,OpenAI还为了她做了网站,贴图,环境画风是这样的: 文中说这环境非常适合PPO-Lagrange style的方法。 这个环境agent可以选...
【摘要】 本文是关于安全强化学习(Safe Reinforcement Learning)的,从定义、现状(经典算法原理,开源环境&代码),应用场景,高价值研究点归纳这几个方面对Safe RL较为全面的介绍。 安全强化学习(Safe Reinforcement Learning)定义: 广义的定义:考虑安全或风险等概念的强化学习 Definition (specific): Safe Reinforcement Lear...
We use the proposed classification to survey the existing literature, as well as suggesting future directions for Safe Reinforcement Learning. 展开 关键词: reinforcement learning risk sensitivity safe exploration teacher advice 被引量: 119 年份: 2015 ...
Reinforcement learning (RL) has emerged as a particularly promising sub-domain in machine learning, providing a robust and adaptive method for training autonomous cars. By utilizing a system of rewards and penalties, RL enables these vehicles to learn and refine their behaviors through iterative inter...
Survey A Comprehensive Survey on Safe Reinforcement Learning JMLR 2015 Workshop Benchmark Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning arXiv 2021 | Github Benchmarking Safe Exploration in Deep Reinforcement Learning preprint 2019 | Code Multi-Agent Constrained Po...
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 ...
A comprehensive survey on safe reinforcement learning 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 impor... J García,F Fernández - JMLR.org 被引量: 119发表: 2015年 Safe Exploration ...
Risk-sensitive reinforcement learning, Paper, Not Find Code, (Accepted by Machine Learning, 2002) Risk-Sensitive Reinforcement Learning Applied to Control under Constraints, Paper, Not Find Code, (Accepted by Journal of Artificial Intelligence Research, 2005) An actor-critic algorithm for constrained ...
Multi-agent reinforcement learning for safe lane changes by connected and autonomous vehicles: A surveydoi:10.3233/AIC-220316REINFORCEMENT learningLANE changingTRAFFIC safetyAUTONOMOUS vehiclesMOTION control devicesDRIVERLESS carsEVIDENCE gapsConnected Autonomous vehicles (CAVs) are expected to improv...