Furthermore, we propose a multi-armed bandit-based vehicle selection algorithm to minimize the utility function considering training loss and delay. The simulation results show that compared with baselines, the proposed algorithm can achieve better training performance with approximately 28\% faster ...
每一行是一个carousel，这种carousel的排序基本上都是基于contextual bandit。有的customer可能推荐viewed pro...
反映在代码上,则是环境生成函数nonstationary_bandit_generate需要被包括在异步执行的函数incremental_epsilon_mab中。完整代码如下: frommultiprocessingimportPoolimportmatplotlib.pyplotaspltimporttimeimportnumpyasnpnp.random.seed(2)TIME_STEP=10000ARM_NUM=10EPSILON=0.1REPITITION=300WORKER=10STEP_PARAM=0.1NONSTATIONARY...
这一章围绕着多臂**机问题,介绍了基本的强化学习算法(value based),并探讨了利用(exploit)和探索(explore)问题。 2.1 A k-armed Bandit Problem 有k个**机,每次的操作就是拉下其中一个控制杆,随后你会得到一个奖励。通过多次的... 查看原文 《强化学习Sutton》读书笔记(一)——多臂赌博机(Multi-armed Ban...
关键词: Markov processes cognitive radio Bayesian RMAB technique Markov chain UCB algorithm nonBayesian RMAB restless multiarmed bandit-based cognitive radio two-slot GCB technique two-slot greedy confidence bound algorithm upper confidence bound algorithm ...
Thus, the requester faces a dilemma of exploration (learning the qualities of the experts) versus exploitation (choosing the experts optimally based on the learnt qualities). A natural solution to this problem can be explored using techniques developed for the multi-armed bandit (MAB) problems [6...
This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in...
We introduce a new multi-armed bandit-based scheduling with a packet cloning mechanism and an (upper bound) delay factor that is pledging for dynamic scheduling and congestion avoidance. The proposed approach is to support flexible runtime control and its capability to respond intelligently concerning...
What is the multi-armed bandit problem? MAB is named after a thought experiment where a gambler has to choose among multiple slot machines with different payouts, and a gambler’s task is to maximize the amount of money he takes back home. Imagine for a moment that you’re the gambler. ...
ficult than necessary for modelling applications like bandit- based decisions in computer Go. Also, regret bounds from previous theoretical work on contextual multi-armed bandits do not satisfy our technical goals described below. Goals: In the stochastic multi-armed bandit problem, each arm is as...