Near-Optimal and Learning-Driven Task Offloading in a 5G Multi-Cell Mobile Edge CloudTask offloadingMobile edge computingApproximation algorithmWith development well underway, 5G is envisioned as an enabler of
Domingo C. Faster near-optimal reinforcement learning:adding adaptiveness to the E^3 algorithm[C]// Proceedings of the 10th International Conference on Algorithmic Learning Theory, Volume 1720 of Lecture Notes in Computer Science,Springer. 1999:241- 251....
本文属于强化学习领域,研究的是比较热门的 extensive-form games with imperfect information,这个设定和我们在本专栏之前的文章里提到的 POMDP (Partially Observable Markov Decision Process) 是一个意思,即所有的玩家只能基于对该环境的真实 state的部分信息来制定自己的策略,在现实生活中有许许多多的游戏都符合这一要...
Not all memories are created equal: learning to forget by expiring 智能生信 2021/11/02 4790 Hierarchical Disentangled Representations 其他 https://arxiv.org/abs/1804.02086 Abstract Deep latent-variable models learn representa-tions of high-dimensional data in an unsuper-vised manner. A number of ...
简介:AI:2020年6月24日北京智源大会演讲分享之机器学习前沿青年科学家专题论坛——10:40-11:10金驰《Near-Optimal Reinforcement Learning with Sel》 导读:首先感谢北京智源大会进行主题演讲的各领域顶级教授,博主受益匪浅,此文章为博主在聆听各领域教授或专家演讲时,一张一张截图进行保存,希望与大家一起学习,共同进步...
【LMPC】《Near-Optimal Rapid MPC Using Neural Networks: A Primal-Dual Policy Learning Framework》 这是一篇关于时变的论文。最近有点浮躁,读书比较慢,这才大四就有点松懈了,非常惭愧。 Introduction 对于线性时不变系统,我们可以离线计算一个最优控制率(所谓的Explicit MPC),但是对于线性时变系统,我们无法得到...
"Selecting near- optimal approximate state representations in reinforcement learning". In: International Conference on Algorithmic Learning Theory. Springer. 140-154.R. Ortner, O.-A. Maillard, and D. Ryabko. Selecting near-optimal approximate state representations in reinforcement learning. In ...
We consider the Bayesian active learning and experimental design problem, where the goal is to learn the value of some unknown target variable through a sequence of informative, noisy tests. In contrast to prior work, we focus on the challenging, yet practically relevant setting where test outcome...
A near-optimal guidance law using deep learning is proposed to intercept the evader inside the capture zone. For the games that start outside the barrier, a learning algorithm for the capture zone embedding strategy is presented based on deep reinforcement learning to help the game state cross ...
In this context, we examine the question of statistical efficiency in kernel-based RL within the reward-free RL framework, specifically asking: how many samples are required to design a near-optimal policy? Existing work addresses this question under restrictive assumptions about the class of kernel...