来源:IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 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...
Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systemsDeep reinforcement learningOne-shot learningNetwork optimizationOnline classificationIn recent years there has been a sharp rise in applications, in which significant events need to be classified but only a...
it doesn’t seem intelligent any more. But deep Q-networks still continue to amaze me. Watching them figure out a new game is like observing an animal in the wild – a rewarding experience by itself.
reinforce- ment learning algorithms bring the hope for machines to have the human-like abilities by directly learning dexterous manipulation from raw pixels. In this review paper, we address the current status of reinforcement learning algorithms used in the field. We...
Yike Guo. Hao’s research chiefly involves Deep Learning and Computer Vision, with the goal of reducing the amount of data required for learning intelligent systems. He is passionate about popularizing artificial intelligence technologies and established TensorLayer, a deep learning and reinforcement ...
This paper proposes a knowledge-enhanced deep reinforcement learning (DRL) method for intelligent ELS. Firstly, the Markov decision process (MDP) of the knowledge-enhanced DRL model for ELS is established based on...
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning. machine-learning reinforcement-learning deep-learning unity unity3d deep-rein...
《An Introduction to Deep Learning: From Perceptrons to Deep Networks》 介绍:深度学习概述:从感知机到深度网络,作者对于例子的选择、理论的介绍都很到位,由浅入深。翻译版本:http://www.cnblogs.com/xiaowanyer/p/3701944.html 《The LION Way: Machine Learning plus Intelligent Optimization》 介绍:<机器学...
In this paper, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as ...
Learning-based方法可以有效地减少人力投入,这在大规模云服务系统中是至关重要的,但目前的研究很少兼顾资源浪费(由于服务负载的变化)和SLO的保证 [14]。而这在生产云环境中却十分常见,例如蚂蚁自身的在线支付系统需要数以万计的容器以满足一天中某些时段的峰值需求,而在其余时间只需要几千个容器,在夜间可能需求更少...