Learning in Congestion Games with Bandit Feedback

In this paper, we investigate Nash-regret minimization in congestion games, a class of games with benign theoretical structure and broad real-world applications. We first propose a centralized algorithm based on the optimism in the face of uncertainty principle for congestion games with (semi-)bandit feedback, and obtain finite-sample guarantees. Then we propose a decentralized algorithm via a novel combination of the Frank-Wolfe method and G-optimal design. By exploiting the structure of the congestion game, we show the sample complexity of both algorithms depends only polynomially on the number of players and the number of facilities, but not the size of the action set, which can be exponentially large in terms of the number of facilities. We further define a new problem class, Markov congestion games, which allows us to model the non-stationarity in congestion games. We propose a centralized algorithm for Markov congestion games, whose sample complexity again has only polynomial dependence on all relevant problem parameters, but not the size of the action set.Comment: 34 pages, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

Defense Technical Information Center thesaurus

This DTIC Thesaurus provides a basic multidisciplinary subject term vocabulary used by DTIC to index and retrieve scientific and technical information from its various data bases and to aid DTIC`s users in their information storage and retrieval operations. It includes an alphabetical posting term display, a hierarchy display, and a Keywork Out of Context (KWOC) display

Generalized Mirror Descents with Non-Convex Potential Functions in Atomic Congestion Games

Abstract. When playing specific classes of no-regret algorithms (especially, multiplicative updates) in atomic congestion games, some previous convergence analyses were done with a standard Rosenthal potential function in terms of mixed strategy profiles (probability distributions on atomic flows), which may not be convex. In several other works, the convergence analysis was done with a convex potential function in terms of nonatomic flows as an approximation of the Rosenthal one in terms of distributions. It can be seen that though with different techniques, the properties from convexity help there, especially for convergence time. However, it would be always a valid question to ask if convergence can still be guaranteed directly with the Rosenthal potential function, playing mirror descents individually in atomic congestion games. We answer this affirmatively by showing the convergence, individually playing discrete mirror descents with the help of the smoothness property similarly adopted in many previous works for congestion games and Fisher (and some more general) markets and individually playing continuous mirror descents with the separability of regularization functions

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This cumulative index of Tech Briefs contains abstracts and four indexes (subject, personal author, originating center, and Tech Brief number) and covers the period 1986 to 1990. The abstract section is organized by the following subject categories: electronic components and circuits, electronic systems, physical sciences, materials, computer programs, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences