This tutorial contains a simple example of how to build a policy-gradient based agent that can solve the contextual bandit problem. For more information, see this Medium post. For more Reinforcement Learning algorithms, including DQN and Model-based learning in Tensorflow, see my Github repo, Dee...
这就是多臂赌博机问题(Multi-armed bandit problem, MAB)。 MAB问题的难点是Exploitation-Exploration(E&E)两难的问题:对已知的吐钱概率比较高的老虎机,应该更多的去尝试(exploitation),以便获得一定的累计收益;对未知的或尝试次数较少的老虎机,还要分配一定的尝试机会(exploration),以免错失收益更高的选择,但同时较多...
多臂赌博机问题(Multi-armed bandit problem, MAB)。 Bandit算法是一类用来实现Exploitation-Exploration机制的策略。根据是否考虑上下文特征,Bandit算法分为context-free bandit和contextual bandit两大类。 Context-free Bandit算法有很多种,比如 、softmax、Thompson Sampling、UCB(Upper Confidence Bound)等。 UCB这样的con...
这就是多臂赌博机问题(Multi-armed bandit problem, MAB)。 MAB问题的难点是Exploitation-Exploration(E&E)两难的问题:对已知的吐钱概率比较高的老虎机,应该更多的去尝试(exploitation),以便获得一定的累计收益;对未知的或尝试次数较少的老虎机,还要分配一定的尝试机会(exploration),以免错失收益更高的选择,但同时较多...
In the traditional contextual bandit problem, the algorithm iteratively chooses an action based on the observed context, and immediately receives a reward for the chosen action. Motivated by a practical need in many applications, we study the design of algorithms under the piled-reward setting, ...
这就是多臂赌博机问题(Multi-armed bandit problem, MAB)。 MAB问题的难点是Exploitation-Exploration(E&E)两难的问题:对已知的吐钱概率比较高的老虎机,应该更多的去尝试(exploitation),以便获得一定的累计收益;对未知的或尝试次数较少的老虎机,还要分配一定的尝试机会(exploration),以免错失收益更高的选择,但同时较多...
K-armed contextual bandit problem 在第t∈[T]轮,智能体观察到由K个特征向量组成的context:{x{t,a}∈R d | a∈[K]}。智能体选择动作a_t并获得奖励r{t,a_t} 目标:最小化 Algorithm 为了学习回报函数,我们 下面正式介绍算法: 通过根据适当的高斯分布随机生成θ的每个项从而初始化网络~ https://icml....
In the contextual bandit framework, multiple arms represent different actions or strategies the agent can take, and each arm provides a certain reward based on the context or environment it is pulled in. The agent receives a contextual observation or information before each decision and aims to se...
In this paper we propose the multi-objective contextual bandit problem with similarity information. This problem extends the classical contextual bandit problem with similarity information by introducing multiple and possibly conflicting objectives. Since the best arm in each objective can be different given...
This Python package contains implementations of methods from different papers dealing with contextual bandit problems, as well as adaptations from typical multi-armed bandits strategies. It aims to provide an easy way to prototype and compare ideas, to reproduce research papers that don't provide easi...