No-regret learning for repeated non-cooperative games with lossy bandits

05/14/2022
by   Wenting Liu, et al.
0

This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback. Since it is difficult to give the explicit model of the utility functions in dynamic environments, the players' action can only be learned with bandit feedback. Moreover, because of unreliable communication channels or privacy protection, the bandit feedback may be lost or dropped at random. Therefore, we study the asynchronous online learning strategy of the players to adaptively adjust the next actions for minimizing the long-term regret loss. The paper provides a novel no-regret learning algorithm, called Online Gradient Descent with lossy bandits (OGD-lb). We first give the regret analysis for concave games with differentiable and Lipschitz utilities. Then we show that the action profile converges to a Nash equilibrium with probability 1 when the game is also strictly monotone. We further provide the mean square convergence rate 𝒪(k^-2min{β, 1/6}) when the game is β- strongly monotone. In addition, we extend the algorithm to the case when the loss probability of the bandit feedback is unknown, and prove its almost sure convergence to Nash equilibrium for strictly monotone games. Finally, we take the resource management in fog computing as an application example, and carry out numerical experiments to empirically demonstrate the algorithm performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/03/2018

Bandit learning in concave N-person games

This paper examines the long-run behavior of learning with bandit feedba...
research
03/28/2022

Distributed Task Management in the Heterogeneous Fog: A Socially Concave Bandit Game

Fog computing has emerged as a potential solution to the explosive compu...
research
12/06/2021

Optimal No-Regret Learning in Strongly Monotone Games with Bandit Feedback

We consider online no-regret learning in unknown games with bandit feedb...
research
06/27/2023

Semi Bandit Dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees

In this work, we introduce a new variant of online gradient descent, whi...
research
06/13/2022

No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation

We examine the problem of regret minimization when the learner is involv...
research
06/19/2020

Gradient-free Online Learning in Games with Delayed Rewards

Motivated by applications to online advertising and recommender systems,...
research
06/09/2020

Stochastic matrix games with bandit feedback

We study a version of the classical zero-sum matrix game with unknown pa...

Please sign up or login with your details

Forgot password? Click here to reset