arXiv research

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

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

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.

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

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.

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

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

New algorithm for multi-player bandits with selfish players, achieving logarithmic regret.

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

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

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…

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.

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

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 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…

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

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…

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

The paper tackles multi-player information asymmetry bandits in metric spaces.

New algorithms improve dueling bandit performance in multiplayer settings.

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

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…

This paper tackles global Nash equilibrium in non-convex multi-player games.

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…

Algorithm optimizes multi-player learning with noisy rewards without direct communication.

Paper solves learning imperfect-information games with fewer episodes.

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…

First sample-efficient algorithm for learning EFCE in bandit feedback settings.

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 consider a symmetric multi-players zero-sum game with two strategic variables. There are $n$ players, $n\geq 3$. Each player is denoted by $i$. Two strategic variables are $t_i$ and $s_i$, $i\in \{1, \dots, n\}$. They are related by invertible functions. Using the minimax theorem by \cite{sion} we will show that Nas…

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…

Computing Nash equilibrium (NE) of multi-player games has witnessed renewed interest due to recent advances in generative adversarial networks. However, computing equilibrium efficiently is challenging. To this end, we introduce the Gradient-based Nikaido-Isoda (GNI) function which serves: (i) as a merit function, vani…

The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 m…

We introduce CSE for MLSF games and devise online learning algorithms for achieving no-external Stackelberg-regret.

This paper shows how to learn variational inequalities fast with strong monotonicity.

Novel algorithms for multi-agent reinforcement learning reduce sample complexity.

We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connection to prove sample complexity lower bounds for locally differentially private protocols as straightforward corollaries of results from com…

Study shows how multiple traders can trade together without excessive price impact.

We introduce a new type of graphical model called a "cumulative distribution network" (CDN), which expresses a joint cumulative distribution as a product of local functions. Each local function can be viewed as providing evidence about possible orderings, or rankings, of variables. Interestingly, we find that the condi…

Data-driven modeling increasingly requires to find a Nash equilibrium in multi-player games, e.g. when training GANs. In this paper, we analyse a new extra-gradient method for Nash equilibrium finding, that performs gradient extrapolations and updates on a random subset of players at each iteration. This approach prova…

This paper improves sample efficiency for learning equilibria in multi-player games.

New algorithms tackle RKHS bandits with reduced complexity and improved performance.

Unified formulation bridges adversarial and nonstationary bandits.

Paper tackles LDP bandits learning with improved results and sub-linear regret.

Paper solves stochastic contextual linear bandits using linear bandit algorithms.