"A review of cooperative multi-agent deep reinforcement learning." Applied Intelligence 53.11 (2023): 13677-13722. arxiv.org/pdf/1908.0396 什么是论文的主要焦点? 论文的主要焦点是合作多智能体深度强化学习(Cooperative Multi-Agent Deep Reinforcement Learning)。它主要讨论了五种常见的解决合作多智能体强化...
As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this ...
IRL——从观测的trajectories中估计未知的奖励函数 memory and attention——在DQN中引入RNN,也就是DRQN(deep recurrent Q-network),这样网络能利用长时间的信息处理POMDPs;再引入attention,也就是DARQN(deep attention recurrent Q-network)。但是在游戏场景中(需要快速的反应,Q值会快速变化),DQN要比DRQN和DARQN更好。
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...
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works ha
Reinforcement learning gets optimal policy through trial-and-error and interaction with dynamic environment. Its properties of self-improving and online le... GAO Yang,CHEN ShiFu,LU Xin,... 被引量: 113发表: 2004年 Research Review on Deep Reinforcement Learning for Solving End-to-End Navigation ...
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Deep learning is revolutionising the way that many industries operate, providing a powerful method to interpret large quantities of data automatically and relatively quickly. Deterioration is often multi-factorial and difficult to model deterministically
本文是关于 "Deep Learning for Anomaly Detection: A Review" 的阅读笔记。本笔记是在理解的基础上对综述的初步提炼,旨在加深理解,以防遗忘。论文的最大贡献在于将已有的深度异常检测方法归纳到三个框架,11种类别中。笔记的架构大体按照原文来,如下: 第一部分:介绍相关概念、问题的复杂性、挑战、建模、分类体系。
这段话说明了自动驾驶中所面临的复杂性,其中涉及多个任务和高维度的决策空间。为了解决这些问题,强化学习(Reinforcement Learning)成为了一个合适的框架,允许代理通过与环境交互来学习最优策略。 图1 现代自动驾驶系统中的标准组件所列出的各种任务。这些模块所解决的关键问题是场景理解、决策和规划 自动驾驶系统中的感知...