New trekking approach reduces collisions in multi-player bandits.
problem Stochastic multi-armed bandits with many unknown players.
method Trekking approach to minimize collisions and regret.
result Guaranteed constant and sub-linear regret for static and dynamic scenarios.
We consider a symmetric multi-players zero-sum game with two strategic variables. There are n n n players, n ≥ 3 n\geq 3 n ≥ 3 . Each player is denoted by i i i . Two strategic variables are t i t_i t i and s i s_i s i , i ∈ { 1 , … , n } i\in \{1, \dots, n\} i ∈ { 1 , … , n } . They are related by invertible functions. Using the minimax theorem by \cite{sion} we will show that Nas…
A policy for near-optimal multi-player bandits with non-zero collision rewards.
problem Decentralized multi-player bandits with heterogeneous rewards and collisions.
method A policy achieving near-optimal regret in a non-communicative setting.
result Near order-optimal expected regret of O ( log 1 + δ T ) O(\log^{1 + δ} T) O ( log 1 + δ T ) for 0 < δ < 1 0 < δ< 1 0 < δ < 1 . A multi-player bandit system resists adversarial attacks with near-optimal regret.
problem Adversaries attempt to manipulate rewards in a multi-player multi-armed bandit game.
method Players communicate a single bit to resist attacks, achieving near-optimal regret.
result Achieves near-optimal regret of O ( log 1 + δ T + W ) O(\log^{1+δ}T + W) O ( log 1 + δ T + W ) , where W W W is the total time of adversarial attacks. Predict player lifetime value based on engagement metrics.
problem User profiling in video games.
method Survival curves, deep learning, long short-term memory.
result Deep learning predicts player lifetime value.
We consider two-player non-zero-sum stopping games in discrete time. Unlike Dynkin games, in our games the payoff of each player is revealed after both players stop. Moreover, each player can adjust her own stopping strategy according to the other player's action. In the first part of the paper, we consider the game wh…
New algorithm reduces regret in multi-player bandits with collision information.
problem Optimizing decisions in multi-player bandits with collision penalties.
method Developed an algorithm with optimal T \sqrt{T} T regret under collision announcements, and sublinear regret without collision info. result First T \sqrt{T} T -type regret guarantee for non-stochastic multi-player multi-armed bandits with collision information. Paper proposes player roles from match data to help clubs.
problem Difficulty in determining a player's fit for a team's style.
method Identifies 21 player roles from match event data.
result Automatic identification of player roles from match data.
New algorithms for n-player games using a player-centered approach.
problem Learning near-perfect strategies in multiplayer games.
method Player-centered reinforcement learning with Final Adaptation RL.
result FARL is crucial for achieving near-perfect strategies in various games.
New algorithm for multi-player bandits with selfish players, achieving logarithmic regret.
problem Challenges of robustness to selfish players in multi-player bandits.
method First algorithm robust to selfish players achieving logarithmic regret, with or without collision observation.
result Achieved logarithmic regret for robust algorithms to selfish players in multi-player bandits.
We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. T…
Paper uses Apprenticeship Learning to model player behavior in interactive narratives.
problem Understanding and simulating player behavior in interactive narratives.
method Receding Horizon IRL (RHIRL) to learn reward functions and policies.
result RHIRL can learn action sequences and generate behavior similar to specific players.
New algorithm for multi-player bandits in changing environments.
problem Sequential action selection with collisions and adversarial losses.
method First provable Multi-player Bandit algorithm for changing environments.
result Resolves open problem in adversarial multi-player settings.
New algorithm tackles multi-player bandit problems with limited access to arms.
problem Limited access to dynamic local subsets of arms in multi-player multi-armed bandit problems.
method Adopted Upper Confidence Bound (UCB) for exploration-exploitation and distributed optimization for collisions.
result Proposes a decentralized algorithm with near-optimal regret guarantee.
Paper presents Transfer Portal model for accurate player performance predictions.
problem Predicting future player performance after a transfer.
method Personalized neural network and Bayesian updating framework.
result Model generates accurate predictions for player performance at new clubs.
A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.
problem Malicious players colliding with cooperative players to prevent rewards.
method Decentralized and robust algorithm RESYNC for defenders.
result RESYNC algorithm is order-optimal, performing gracefully as the number of collisions increases.
Study predicts soccer player market values using machine learning and SHAP for interpretability.
problem Predicting accurate market values for professional soccer players.
method Ensemble machine learning models, SHAP for interpretability, Boruta for feature selection.
result GBDT model achieved high predictive accuracy (R-squared 0.901, RMSE 3,221,632.175).
Algorithm optimizes multi-player learning with noisy rewards without direct communication.
problem Cooperative multi-player learning with noisy rewards and no communication.
method Upper and lower confidence bounds algorithm for optimal action selection.
result Achieves logarithmic O ( log T Δ a ) O(\frac{\log T}{Δ_{\bm{a}}}) O ( Δ a l o g T ) and O ( T log T ) O(\sqrt{T\log T}) O ( T log T ) regret. New strategy achieves optimal regret without communication or collisions in multi-player bandit.
problem Cooperative multi-player stochastic multi-armed bandit with shared randomness.
method Combination of combinatorial approach to generalize geometric intuition.
result Achieves near-optimal regret i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) for any number of players and arms without collisions. New algorithm for multi-player bandits in decentralized, asynchronous systems.
problem Challenges in decentralized, asynchronous multi-player bandits, including coordination and player detection.
method Adaptive exploration-exploitation algorithm that reduces collisions and detects player presence.
result Achieves regret of O ( T log T + log T / Δ 2 ) \mathcal{O}(\sqrt{T \log T} + {\log T}/{Δ^2}) O ( T log T + log T / Δ 2 ) . Method predicts NBA players' multi-modal movement trajectories.
problem Understanding NBA players' decision-making during games.
method LSTM-based architecture with multi-modal loss function.
result Method outperforms state-of-the-art in predicting realistic trajectories.
New learning dynamics adapt to corrupted games, improving performance in real-world scenarios.
problem Learning dynamics in games are limited to honest players, ignoring real-world corruption.
method Adaptive learning dynamics that adapt to player deviations from prescribed algorithms.
result Learning dynamics achieve better performance in corrupted games, matching honest regime bounds.
Paper presents content-based models for game recommendation in cold start scenarios.
problem Cold start problem in game recommendation where new games and players have no historical data.
method Uses survey data to develop content-based interaction models that generalize to new games, players, and both.
result Content models outperform collaborative filtering in predicting new interactions.
Algorithm reduces regret in multi-player bandits with unknown collision rewards.
problem Reducing regret in multi-player multi-armed bandits with unknown collision rewards.
method Proposes an algorithm that combines a modified successive elimination strategy with a communication protocol to estimate suboptimality gaps and coordinate among players.
result Achieves logarithmic regret for the problem when collision reward is unknown.
Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in …
New framework values football players based on in-game interactions.
problem Valuing football players based on in-game performance.
method Combining financial models and network theory using a passing matrix.
result Dynamic and individualized player valuation framework.
The paper analyzes a game where players must balance short-term and long-term interests, leading to cooperative or competitive outcomes.
problem Analyzing time inconsistency in inter-personal decision-making under non-exponential discounting.
method Iterative procedures and Zorn's lemma to find Nash equilibria between players' intra-personal equilibria.
result Inter-personal equilibria exist and depend on the impatience levels of the players.
Researchers predict NBA player salaries using machine learning, avoiding overfitting.
problem Predicting NBA player salaries based on performance statistics.
method Selected important determinants, used Random Forest machine learning, avoided overfitting.
result Very satisfactory salary predictions identified for important factors.
A variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player performance depends on the game context. This paper proposes a new approach to capturing game context: we apply Deep Reinforcement Learning (…
MpFL models clients as strategic players to reach equilibrium with less communication.
problem Real-world clients act independently with individual objectives, not aligned with a shared global model.
method MpFL uses game-theoretic modeling and PEARL-SGD algorithm for local updates and communication.
result PEARL-SGD reaches an equilibrium with less communication than non-local updates in stochastic setup.
Algorithm aggregates rewards from multiple players to learn related tasks in online bandit learning.
problem Learning related but slightly different tasks in an online setting with heterogeneous feedback.
method RobustAgg ( ε ) (ε) ( ε ) algorithm that aggregates rewards from different players. result Achieves instance-dependent regret guarantees and nearly matching lower bounds.
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…
New algorithm tackles multiplayer bandits with varying arm means, achieving optimal regret.
problem Stochastic multi-armed bandit problem with non-communicating players and collisions.
method Combines forced collisions for implicit communication and matching eliminations.
result First sublinear minimax regret bound of O ( ln ( T ) ) O(\ln(T)) O ( ln ( T )) for unique optimal assignment. Study proposes new OPE estimators for two-player zero-sum games.
problem Evaluating new policies using historical data from a different policy in multi-player zero-sum games.
method Doubly robust and double reinforcement learning estimators to project exploitability.
result Prove exploitability estimation error bounds and regret bounds for policy profiles.
New algorithms tackle adversarial multi-player bandits with forced-collision communication.
problem No-sensing adversarial multi-player multi-armed bandits (MP-MAB) problem.
method Adversary-Adaptive Collision-Communication (A2C2) algorithms, attackability-aware and unaware settings, information-theoretic tools, error-correction coding.
result Asymptotic attackability-dependent sublinear regret achieved, with or without knowing attackability.
Players choose rebalancing rules to maximize their wealth relative to others in a continuous-time trading game.
problem Optimizing wealth in a continuous-time trading game between two players.
method Players choose rebalancing rules to maximize their expected wealth ratio, using the Kelly rule in equilibrium.
result The Kelly rule emerges as the optimal strategy in both short and long time intervals.
Models predict player motivation from game data.
problem Predict player motivation from gameplay data.
method Collected gameplay data and survey responses, used SVM for inference.
result Models accurately predict player motivation (92%-94%).
Develops a framework to estimate NBA player salary ROI.
problem Measuring the relative return of player salaries in NBA.
method Five-part framework: GCP measure, SGV calculation, cash flow series, ROI calculation.
result Illustrates framework with 2022-2023 NBA data, showing top and bottom performers.
Study of a game with multiple players and common shocks using probabilistic methods.
problem Analyze a game with multiple players and common shocks.
method Probabilistic approach to study the game, including mean field and FBSDEs.
result Unique equilibrium found for both N-player and mean field games.
SciSports is a Dutch startup company specializing in football analytics. This paper describes a joint research effort with SciSports, during the Study Group Mathematics with Industry 2018 at Eindhoven, the Netherlands. The main challenge that we addressed was to automatically process empirical football players' traject…
Study multi-armed bandits with compensation to maximize total reward and minimize payments.
problem Optimize reward collection from short-term players with compensation.
method Propose KCMAB problem, provide lower bound, analyze three algorithms, and demonstrate performance.
result Algorithms achieve O(log T) regret and O(log T) compensation matching theoretical lower bound.
Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.
problem Non-cooperative games with globally uncertain parameters and no common prior.
method Mixed strategies and subjective priors, Extended Equilibrium defined by fixed-point argument.
result Existence of Extended Equilibrium under certain conditions.
Memory-enhanced model predicts tennis shots based on player history.
problem Predicting shot location and type in tennis.
method Semi-supervised Generative Adversarial Network with neural memory modules.
result The model learns player-specific behavioral patterns from match data.
Two new algorithms reduce group regret in abruptly changing multi-player bandit problems.
problem Reducing group regret in multi-player bandit problems in environments that change suddenly.
method Design of two novel algorithms: RR-SW-UCB# and SW-DLP.
result Expected cumulative group regret converges to zero over time.
New methods rank players using covariates and comparisons, outperforming existing algorithms.
problem Ranking players based on incomplete and noisy pairwise comparisons.
method Three spectral ranking methods incorporating player covariates.
result Proposed methods outperform existing algorithms in simulations.
Graphon game model simplifies stochastic interactions among agents.
problem Complex interactions among heterogeneous agents in stochastic games.
method Introduced a discrete-time graphon game formulation with a representative player.
result Existence and uniqueness of graphon equilibrium proven with mild assumptions.
Study on market entry timing in stock liquidation with trading constraints.
problem Optimal timing of market entry and exit in portfolio liquidation with trading restrictions.
method Mean-field game approach to model N N N -player and mean-field games of optimal portfolio liquidation. result Existence of unique equilibrium in both mean-field and N N N -player games. Two non-communicating players minimize regret in a multi-armed bandit game.
problem Optimal regret in non-communicating multi-armed bandit players.
method Proposed a strategy with no collisions, achieving near-optimal regret.
result Near-optimal regret of O ( T log ( T ) ) O(\sqrt{T \log(T)}) O ( T log ( T ) ) with very high probability.