This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, whic
Although deep reinforcement learning has become a promising machine learning approach for sequential decision-making problems, it is still not mature enough for high-stake domains such as autonomous driving or medical applications. In such contexts, a learned policy needs for instance to be interpretabl...
On-policy方法有SARSA,使用相同的策略进行控制以及策略价值的估计。Off-policy有Q-learning,有behavior policy以及target policy,前者用来生成动作,后者用来被改进。target policy可能是确定并且贪心的,behavoir policy可以用来探索。 Deep reinforcement learning (DRL) DQN不介绍了,DQN中简单的回放经验,只是使用相同频率进行...
A Survey on Deep Learning for Named Entity Recognition 用于命名实体识别的深度学习综述 tkde 2022文章链接:github.com/ICTKC/Papers Abstract 命名实体识别(NER)的任务是识别 mention 命名实体的文本范围,并将其分类为预定义的类别,例如人,位置,组织等。近年来,由连续实值向量表示和通过非线性处理的语义组合赋予的...
Index Terms——Deep reinforcement learning, Autonomous driving, Imitation learning, Inverse reinforcement learning, Controller learning, Trajectory optimisation, Motion planning, Safe reinforcement learning. I. INTRODUCTION 自动驾驶(AD)1系统由多个感知级别的任务组成,由于深度学习架构,这些任务现在已经实现了高精度...
So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(ED) such as IoT, Webcam, Drone, Intelligent Medical Equipment, Robot, etc. are very useful in a pandemic situation. These types of equipment make the ...
Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems. In this survey, we systematically ...
2. Deep RL in 行为决策 和 运动规划 典型的pipeline是,输入传感器数据流,辅以全局路径规划信息,处理后最终得到控制输出(转角、加速度),这种处理的流程一般是分层的,因为驾驶动作天然是分级的,先是一个高级的离散状态的决策(行为决策,换道、跟车、左转),接着一个连续状态空间的动作(运动规划,提供能满足behavior的...
可解释深度强化学习综述 A Survey on Explainable Deep Reinforcement Learning 热度: 92 ASurveyonDeepLearning:Algorithms,Techniques, andApplications SAMIRA POUYANFAR, FloridaInternational UniversitySAAD SADIQ and YILIN YAN, UniversityofMiamiHAIMAN TIAN, Florida ...
链接:Deep Reinforcement Learning for Autonomous Driving: A Survey 目前在已经被引950次这篇论文总结了深度强化学习(DRL)算法,并提供了一个自动驾驶任务的分类,其中(D)RL方法已经得到应用。论文还讨论了在实际部署自动驾驶代理时所面临的关键计算挑战,并概述了与经典强化学习算法相关但不同的相邻领域,例如行为克隆、...