arXiv research

Algorithm reduces regret in multi-player bandits with unknown collision rewards.

A new algorithm reduces regret in multi-player bandits without collision info.

New algorithm tackles multi-player bandit problems with limited access to arms.

New strategy achieves optimal regret without communication or collisions in multi-player bandit.

New algorithm for multi-player bandits without needing lower bounds or scaling inversely.

New algorithms tackle adversarial multi-player bandits with forced-collision communication.

New algorithm for multi-player bandits with collision-dependent rewards.

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first $\sqrt{T}$-type regret guarantee for th…

No communication allows optimal instance-dependent regret guarantees in multi-player bandits.

New algorithm outperforms existing ones in multi-player bandit problems without sensing.

A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.

Paper closes the gap in MP-MAB problems with novel adaptive communication and exploration.

A multi-player bandit system resists adversarial attacks with near-optimal regret.

We consider a fully decentralized multi-player stochastic multi-armed bandit setting where the players cannot communicate with each other and can observe only their own actions and rewards. The environment may appear differently to different players, $\textit{i.e.}$, the reward distributions for a given arm are heterog…

Multi-player Multi-Armed Bandits (MAB) have been extensively studied in the literature, motivated by applications to Cognitive Radio systems. Driven by such applications as well, we motivate the introduction of several levels of feedback for multi-player MAB algorithms. Most existing work assume that sensing informatio…

New algorithm for multi-player bandits in decentralized, asynchronous systems.

Algorithm aggregates rewards from multiple players to learn related tasks in online bandit learning.

New algorithms improve dueling bandit performance in multiplayer settings.

Motivated by cognitive radios, stochastic multi-player multi-armed bandits gained a lot of interest recently. In this class of problems, several players simultaneously pull arms and encounter a collision - with 0 reward - if some of them pull the same arm at the same time. While the cooperative case where players maxim…

We consider a variant of the stochastic multi-armed bandit problem, where multiple players simultaneously choose from the same set of arms and may collide, receiving no reward. This setting has been motivated by problems arising in cognitive radio networks, and is especially challenging under the realistic assumption t…

We study stochastic multi-armed bandits with many players. The players do not know the number of players, cannot communicate with each other and if multiple players select a common arm they collide and none of them receive any reward. We consider the static scenario, where the number of players remains fixed, and the d…

We consider a setting where multiple players sequentially choose among a common set of actions (arms). Motivated by a cognitive radio networks application, we assume that players incur a loss upon colliding, and that communication between players is not possible. Existing approaches assume that the system is stationary…

Optimistic Thompson Sampling reduces regret in unknown multi-player games.

Next generation networks are expected to be ultradense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum. Such ultra-dense networks will incur significant signaling load at base stations leading to a negative effect on spectrum and energy ef…

We study the multi-player stochastic multiarmed bandit (MAB) problem in an abruptly changing environment. We consider a collision model in which a player receives reward at an arm if it is the only player to select the arm. We design two novel algorithms, namely, Round-Robin Sliding-Window Upper Confidence Bound\# (RR-…

We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round. Apart from the usual trade-off between exploring new arms to find the best one and exploiting the arm believed to offer the highest reward, we encounter an addi…

New dynamic allocation methods for multi-armed bandit models.

PAC-Bayesian analysis improves lifelong learning in multi-armed bandits.

Paper uses subjective logic to estimate uncertainty in multi-armed bandit problems.

Algorithm improves multi-armed bandit performance by transferring reward samples.

Quantum algorithms for multi-armed bandits are explored with limited reward access.

Study on Pareto optimality in multi-objective bandit problems.

Unified formulation bridges adversarial and nonstationary bandits.

In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi…

We consider the problem of learning in single-player and multiplayer multiarmed bandit models. Bandit problems are classes of online learning problems that capture exploration versus exploitation tradeoffs. In a multiarmed bandit model, players can pick among many arms, and each play of an arm generates an i.i.d. rewar…

Study finds optimal regret bound for multi-armed bandit problem with expert advice.

The paper tackles lifelong learning in multi-armed bandits, aiming to minimize average regret over multiple tasks.

We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information. Algorithms that are designed solely for one of the regimes are known to be sub-optimal for the alternate regime…

New method for identifying best arm in batched multi-armed bandit problems.

Study collaborative learning among multi-agents in multi-armed bandits.

The paper explores sampling problems and shows minimal exploration is needed.

New algorithm for nonstationary multi-armed bandits with optimal performance.

Paper tackles transfer learning for contextual multi-armed bandits under covariate shift.

Survey on multiplayer bandits, highlighting theoretical gaps and future directions.

Proposes Genetic Thompson Sampling for multi-armed bandits, improving performance in nonstationary settings.

New method for contextual bandits with corrupted context.

We present a formal model of human decision-making in explore-exploit tasks using the context of multi-armed bandit problems, where the decision-maker must choose among multiple options with uncertain rewards. We address the standard multi-armed bandit problem, the multi-armed bandit problem with transition costs, and …

In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for the two-armed stochastic bandits has been completely characterized in \cite{perchet2016batched}, the effect of the number of arms on the re…