Efficient Contextual Bandits with Continuous Actions

Maryam Majzoubi; Chicheng Zhang; Rajan Chari; Akshay Krishnamurthy; John Langford; Aleksandrs Slivkins

doi:10.48550/arxiv.2006.06040

NeurIPS2020顶会

Efficient Contextual Bandits with Continuous Actions

Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins

arXiv DOI 出版方

2020年份

39被引次数

13顶会引用

摘要

We create a computationally tractable algorithm for contextual bandits with continuous actions having unknown structure. Our reduction-style algorithm composes with most supervised learning representations. We prove that it works in a general sense and verify the new functionality with large-scale experiments.

问问这篇 Paper

智能体会读完全文。

Lune 把这篇 Paper 索引到了每一个公式，引用它的顶会 Paper 也一样。你提问，回答直接引用原文。

核心论断
谁在沿用
横向对比
局限

你的问题

智能体调用

Luneget_paper_fulltext

在 Lune 里问

免费开始，无需绑卡

引用它的顶会 Paper13

Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution NetworksJianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song 等NeurIPS 2021 · 被引用 216 次
Contextual Bandits with Large Action Spaces: Made PracticalYinglun Zhu, Dylan J. Foster, John Langford, Paul MineiroICML 2022 · 被引用 34 次
Deep Hierarchy in BanditsJoey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer 等ICML 2022 · 被引用 21 次
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action SpacesYinglun Zhu, Paul MineiroICML 2022 · 被引用 19 次
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsHengrui Cai, Chengchun Shi, Rui Song, Wenbin LuNeurIPS 2021 · 被引用 18 次

问问它们各自怎么用它

摘要

问问这篇 Paper

智能体会读完全文。

引用它的顶会 Paper13

相关 Paper