给出一个例子:假设有好几个不同的k-armed bandit问题,每次选择后你将随机面对不同一个问题,这种问题使用我们之前的方法不能很好地解决,除非真实action value变化的很慢。 假设你有一个策略(policy)当出现一种情况,你就可以根据它选择该状态下最好的action,这就是关联搜索问题(associative search task),在学术上经...
associative search task包括trial and error,search for the best actions和association,也称为contextual bandits。此类问题像full RL problem,包括学习一个policy,也想bandit problem,使用immediate reward。 2.10 Summary 本章列了一些平衡exploration and exploitation的简单方法:epsilon-greedy,UCB,gradient bandit algorith...
Pini, G., Brutschy, A., Francesca, G., Dorigo, M., Birattari, M.: Multi- armed Bandit Formulation of the Task Partitioning Problem in Swarm Robotics - Online supplementary material (2012), http://iridia.ulb.ac.be/ supp/IridiaSupp2012-005/...
Multi-task learningadversarial multi-armed bandittask variance regularizationMulti-task Learning (MTL), which involves the simultaneous learning of multiple tasks, can achieve better performance than learning each task independently. It has achieved great success in various applications, ranging from ...
总结: Multi-armed bandit problem(又称k-armed bandit problem)并非完全的reinforcement learning,而只是其简化版本。 所以该书将bandit问题作为引子,引出reinforcement learning的问题。reinforcement learning中的一些概念都是其中的一些概念扩展而来的。
Altered Statistical Learning and Decision-Making in Methamphetamine Dependence: Evidence from a Two-Armed Bandit Task Understanding how humans weigh long-term and short-term goals is important for both basic cognitive science and clinical neuroscience, as substance users n... KM Harlé,S Zhang,S Max...
Here’s where it gets particularly interesting: while intuitively one might think the task of our Multi-armed Bandit algorithms is to unearth that ideal price where the probability of purchase is highest, it’s not quite so straightforward. In fact, our ultimate goal is to maximize the revenu...
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. ...
(2005). “Multi-armed Bandit Algorithms and Empirical Evaluation.” In: Proceedings of the 16th European conference on Machine Learning. doi:10.1007/11564096_42 Subject Headings: Multi-Armed Bandit Algorithm; Multi-Armed Bandit Task. Notes Cited By http://scholar.google.com/scholar?q=%22Multi-...
8.Dependent Task Placement and Scheduling with Function Configuration in Edge Computing9.Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning10.Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach11.Towards Revenue-Driven Multi-User ...