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Titlebook: Distributed Optimization, Game and Learning Algorithms; Theory and Applicati Huiwei Wang,Huaqing Li,Bo Zhou Book 2021 The Editor(s) (if app

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樓主: Clinical-Trial
31#
發(fā)表于 2025-3-26 21:40:44 | 只看該作者
32#
發(fā)表于 2025-3-27 02:23:42 | 只看該作者
Reinforcement Learning for PHEV Route Choice Based on Congestion Game,d based on the Nguyen–Dupuis network, the experimental results not only demonstrate the correctness of theoretical results, but also show that the B–M RL-based solution method outperforms several existing solution methods.
33#
發(fā)表于 2025-3-27 06:43:39 | 只看該作者
34#
發(fā)表于 2025-3-27 12:14:16 | 只看該作者
35#
發(fā)表于 2025-3-27 15:12:16 | 只看該作者
36#
發(fā)表于 2025-3-27 20:42:11 | 只看該作者
The Statesman‘s Year-Book, 1996-7ex functions over strongly connected and directed graphs. A novel distributed algorithm is proposed where both row and column-stochastic matrices are utilized to bypass the limits of the implementation of doubly-stochastic matrices or eigenvector estimation in related work. Besides, it has an eviden
37#
發(fā)表于 2025-3-27 23:31:20 | 只看該作者
The Statesman‘s Year-Book, 1996-7 of all local convex objective functions. Each agent in the network possesses only its private local convex objective function and is constrained to a coupling equality constraint and its local inequality constraint. Moreover, we particularly focus on the scenario where each agent is only allowed to
38#
發(fā)表于 2025-3-28 03:50:06 | 只看該作者
The Statesman‘s Year-Book, 1996-7eting total demands and complying with individual generator output constraints. This chapter proposes a distributed optimization algorithm with noisy gradient based on stochastic gradient-push approach to solve the EDP on time-varying directed communication networks potentially with time delays. It
39#
發(fā)表于 2025-3-28 09:54:09 | 只看該作者
40#
發(fā)表于 2025-3-28 11:25:38 | 只看該作者
The Statesman‘s Year-Book, 1996-7among individually strategic players with incomplete information. In this game, each player uses the learning automaton (LA) scheme to generate the action probability distribution based on his/her private information for maximizing own averaged utility. It is shown that if one of admissible mixed-st
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