arXiv research

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

New algorithms improve dueling bandit performance in multiplayer settings.

New algorithm reduces regret in asynchronous multiplayer bandits to constant or logarithmic levels.

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

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…

Paper develops efficient algorithms for learning rationalizable equilibria in multiplayer games.

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

The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where $M$ decision makers or players collaborate to maximize their cumulative reward. When several players select the same arm, a collision occurs and no reward is collected on this arm. Players involved in a collision are informed about this collis…

We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider the challenging heterogeneous setting, in which different arms may have different means for differe…

Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage. We present a decentralized algorithm that achieves the same performance as a centra…

We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider two feedback models: a model in which the players can observe whether a collision has occurred an…

Paper tackles multiplayer symmetric games, securing equal share for n players.

MpFL models clients as strategic players to reach equilibrium with less communication.

We consider an ad hoc network where multiple users access the same set of channels. The channel characteristics are unknown and could be different for each user (heterogeneous). No controller is available to coordinate channel selections by the users, and if multiple users select the same channel, they collide and none…

V-learning tackles multiagent reinforcement learning by reducing sample complexity.

Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games. However, the real-world contains multiple agents, each learning and acting independently to cooperate and compete with other agents, a…

Trading algorithms that execute large orders are susceptible to exploitation by order anticipation strategies. This paper studies the influence of order anticipation strategies in a multi-investor model of optimal execution under transient price impact. Existence and uniqueness of a Nash equilibrium is established unde…

Stochastic algo learns from evolving data, achieving optimal performance.

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…

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner. We introduce the Google Research Football Environment, a new reinforcement learning environment wher…

Gradient methods converge exponentially in concave network games.

Paper proposes a method to predict MOBA game winners with calibrated confidence.

Bayesian rating system for large competitions improves prediction and efficiency.

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.

Graph-Triggered Bandits unify rested and restless bandits with graph-defined arm interactions.

New definition resolves ambiguity in non-stationary bandit classification.

New algorithms for n-player games using a player-centered approach.

Unified approach for non-stationary and clustered bandits.

A framework for auto-tuning hyper-parameters in contextual bandit algorithms.

Stochastic multi-armed bandits form a class of online learning problems that have important applications in online recommendation systems, adaptive medical treatment, and many others. Even though potential attacks against these learning algorithms may hijack their behavior, causing catastrophic loss in real-world appli…

New algorithm learns optimal exploration parameters for contextual bandits.

Investigates sequential problems on graph structures and large action spaces.

Study on indexability of restless multi-armed bandits and rollout policy performance.

New insights into multi-armed bandits with budget constraints.

Optimal algorithm for identifying best arm in stochastic linear bandits with fixed confidence.

A meta-UCB method combines stochastic bandit algorithms.

New method for contextual bandits with corrupted context.

A new framework for structured bandits using influence diagrams and variational Thompson sampling.

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

First robust bandit algorithm for contextual bandits with sub-linear regret.

Unified framework for high-dimensional bandit problems with low-dimensional structures.

Survey and compare PAC-Bayes bounds for bandit problems.

Hyperinflation and price volatility in virtual economies has the potential to reduce player satisfaction and decrease developer revenue. This paper describes intuitive analytical methods for monitoring volatility and inflation in virtual economies, with worked examples on the increasingly popular multiplayer game Old S…

New algorithms reduce regret for convex bandits with small comparator norms.

The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. We present an artificial intelligence research environment, inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMO…