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...
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...
We study the contextual bandit problem with linear payoff function. In the traditional contextual bandit problem, the algorithm iter-atively 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, ...
这就是多臂赌博机问题(Multi-armed bandit problem, MAB)。 MAB问题的核心是Exploitation-Exploration的Trade-Off[1]:即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...
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...
We consider the stochastic contextual bandit problem with additional regularization. The motivation comes from problems where the policy of the agent must be close to some baseline policy which is known to perform well on the task. To tackle this problem we use a nonparametric model and propose ...
We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expected values of these outcomes depend linearly on the context of that arm...
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...