Multi-armed Bandit Policies
Lower Bounds and Selectivity of Weak-Consistent Policies in Stochastic Multi-Armed Bandit Problem
... optimal policies that achieve these lower bounds, as it is the case in the classical class of consistent ...of policies, we define selectivity as the ability to perform at least as good as the policy that ...
21
Optimizing Adaptive Marketing Experiments with the Multi-Armed Bandit
... the Multi-Armed Bandit Abstract Sequential decision making is central to a range of marketing ...the multi-armed bandit, the conceptual and methodological backbone of this ...
148
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
... A policy is a mapping that given a history, chooses a particular coin to be tried next, or selects a particular coin and stops. We allow a policy to use randomization when choosing the next coin to be tried or when ...
26
The Non-stationary Stochastic Multi-armed Bandit Problem
... the multi-armed bandit problem that generalize the stationary stochastic, piecewise- stationary and adversarial bandit ...switching bandit problem with SER4 by adding a probability of ...
21
Slow Fading Channel Selection: A Restless Multi-Armed Bandit Formulation
... First, we consider 3 arms, where arms 2,3 are statistically equivalent, and ϕ 2 = ϕ 3 = 0.3, σ 2 2 = σ 2 3 = 1, and m 2 = m 3 = 8. Arm 1 has the same coefficients ϕ 1 = 0.3, σ 2 1 = 1 as arms 2,3. In Figure 1 we show the ...
5
Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments
... The key benefit of partial pooling is capturing heterogeneity across websites, but an added benefit is providing a predictive distribution for the ads on any website in question, even in the absence of a large amount of ...
69
Selecting Multiple Web Adverts - a Contextual Multi-armed Bandit with State Uncertainty
... Contextual Multi-armed Bandit with State Uncertainty Abstract We present a method to solve the problem of choosing a set of adverts to display to each of a sequence of web ...combinatorial ...
31
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
... When an action in a certain state can be determined to not belong to the optimal policy in an MDP, it can be discarded and disregarded in both planning and learning. This idea, commonly known as action elimination (AE), ...
27
Transfer restless multi armed bandit policy for energy efficient heterogeneous cellular network
... our policies have been shown to be able to follow a practical periodic traffic fluc- ...index-based policies, such as Thomson sampling or Bayesian-UCB, that are known for their ...
19
Transfer Restless Multi-Armed Bandit Policy for Energy Efficient Heterogeneous Cellular Network
... Figure 5 Improvement gain of TLEEM-UCB policy w.r.t. EEM-UCB policy for different target arrival rate. The bars corresponding to the left Y-axis reflect the gain in CEER while the right Y-axis represents the difference Λ ...
28
Investigación Operativa. Multi-armed restless bandits, index policies, and dynamic priority allocation
... “one-armed bandit” is used to refer to a slot machine, of the kind one finds in a casino, the “arm” being the lever that the gambler pulls after tossing in the ...
10
Algorithms for the multi-armed bandit problem
... stochastic multi-armed bandit problem is an important model for studying the exploration- exploitation tradeoff in reinforcement ...popular multi-armed bandit ...of bandit ...
32
Monotone multi-armed bandit allocations
... Peter Auer, Nicol` o Cesa-Bianchi, Yoav Freund, and Robert E. Schapire. The nonstochastic multi- armed bandit problem. SIAM J. Comput., 32(1):48–77, 2002b. Preliminary version in 36th IEEE FOCS, ...
5
Multi-armed Bandit Problems with History
... More generally, we define historic data as any obser- vations of the arms that are collected before the start of the online learning algorithm. The algorithm itself has no control over the choice of arms in the historic ...
11
Mechanisms with learning for stochastic multi armed bandit problems
... A single pull sleeping bandit variant for crowdsourcing is considered in [7]. Here each task assigned to the worker has a strict deadline until which the worker is not available for other tasks. They further ...
44
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
... a Multi-Armed Ban- dit (MAB) problem with a finite reward population via the Gumbel-Max trick (Papandreou & Yuille, 2011), and then propose three algorithms with theoretical guarantees on the approximation ...
17
Towards an Improved Strategy for Solving Multi Armed Bandit Problem
... Abstract: Multi-Armed Bandit (MAB) problem is one of the classical reinforcements learning problems that describe the friction between the agent’s exploration and ...
5
A multi-armed bandit approach for exploring partially observed networks
... a multi-armed bandit based exploration algorithm for par- tially observed incomplete ...nonparametric multi-armed bandit algorithm iKNN-UCB with sublinear ...iKNN-UCB ...
18
muMAB. A multi-armed bandit model for wireless network selection
... a multi-RAT environment, in order to quantitatively assess its accuracy in combination with different utility metrics to be adopted as reward, including those impacted by user ...
22
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models
... stochastic multi-armed bandit model is a simple abstraction that has proven useful in many different contexts in statistics and machine ...two armed-bandits, we derive refined lower bounds in ...
42