In　this　paper,　we　study　online　diminishing　return　submodular　(DR-submodular　for　short)　maximization　in　the　bandit　setting.　Our　focus　is　on　problems　where　the　reward　functions　can　be　non-monotone,　and　the　constraint　set　is　a　general　convex　set.　We　first　present　the　Single-sampling　Non-monotone　Frank-Wolfe　algorithm.　This　algorithm　only　requires　a　single　call　to　each　reward　function,　and　it　computes　the　stochastic　gradient　to　make　it　suitable　for　large-scale　settings　where　full　gradient　information　might　not　be　available.　We　provide　an　analysis　of　the　approximation　ratio　and　regret　bound　of　the　proposed　algorithm.　We　then　propose　the　Bandit　Online　Non-monotone　Frank-Wolfe　algorithm　to　adjust　for　problems　in　the　bandit　setting,　where　each　reward　function　returns　the　function　value　at　a　single　point.　We　take　advantage　of　smoothing　approximations　to　reward　functions　to　tackle　the　challenges　posed　by　the　bandit　setting.　Under　mild　assumptions,　our　proposed　algorithm　can　reach　14(1-minx　is　an　element　of　P　＇&　Vert;x　&　Vert;infinity)\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$\frac{1}{4}　(1-　\min　_{x\in　{\mathcal　{P}}＇}\Vert　x\Vert　_\infty　)$$\end{document}-approximation　with　regret　bounded　by　O(T5min{1,gamma}+56min{1,gamma}+5)\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$O　(T＜^＞{\frac{5　\min　\{1,　\gamma　\}+5　}{6　\min　\{1,　\gamma　\}+5}})$$\end{document},　where　the　positive　parameter　gamma\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$\gamma　$$\end{document}　is　related　to　the　＂safety　domain＂　P　＇\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$${\mathcal　{P}}＇$$\end{document}.　To　the　best　of　our　knowledge,　this　is　the　first　work　to　address　online　non-monotone　DR-submodular　maximization　over　a　general　convex　set　in　the　bandit　setting.