No-regret learning for repeated non-cooperative games with lossy bandits
From MaRDI portal
Publication:6152576
DOI10.1016/j.automatica.2023.111455arXiv2205.06968MaRDI QIDQ6152576
Wenting Liu, Peng Yi, Yiguang Hong, Jinlong Lei
Publication date: 13 February 2024
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2205.06968
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A one-measurement form of simultaneous perturbation stochastic approximation
- Logarithmic regret algorithms for online convex optimization
- Nash equilibrium seeking for \(N\)-coalition noncooperative games
- Optimal distributed stochastic mirror descent for strongly convex optimization
- An operator splitting approach for distributed generalized Nash equilibria computation
- Generalized Nash equilibrium seeking strategy for distributed nonsmooth multi-cluster game
- Distributed Nash equilibrium seeking under partial-decision information via the alternating direction method of multipliers
- On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms
- A new one-point residual-feedback oracle for black-box learning and control
- Corrigendum to: ``Stochastic generalized Nash equilibrium seeking under partial-decision information
- Predictive online convex optimization
- On the robustness of learning in games with stochastically perturbed payoff observations
- Random gradient-free minimization of convex functions
- Efficient algorithms for online decision problems
- Decentralized online convex optimization based on signs of relative states
- Personalized optimization with user's feedback
- Online Learning and Online Convex Optimization
- Design of Cognitive Radio Systems Under Temperature-Interference Constraints: A Variational Inequality Approach
- An Online Convex Optimization Approach to Proactive Network Resource Allocation
- Game Theory for Big Data Processing: Multileader Multifollower Game-Based ADMM
- Learning with minimal information in continuous games
- Continuous-Time Discounted Mirror Descent Dynamics in Monotone Concave Games
- A Distributed Forward–Backward Algorithm for Stochastic Generalized Nash Equilibrium Seeking
- Distributed Bandit Online Convex Optimization With Time-Varying Coupled Inequality Constraints
- On Synchronous, Asynchronous, and Randomized Best-Response Schemes for Stochastic Nash Games
- Distributed Online Linear Regressions
- Secure Mobile Edge Computing in IoT via Collaborative Online Learning
- Online Convex Optimization With Time-Varying Constraints and Bandit Feedback
- Prediction, Learning, and Games
- Existence and Uniqueness of Equilibrium Points for Concave N-Person Games
- On a Stochastic Approximation Method
- Distributed Stochastic Optimization with Large Delays
This page was built for publication: No-regret learning for repeated non-cooperative games with lossy bandits
