Learning of Nash Equilibria in Risk-Averse Games-Reference-Cited by-同舟云学术

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Learning of Nash Equilibria in Risk-Averse Games

Published:2024-07-10 Issue: Volume:32 Page:3270-3275
ISSN:
Container-title:2024 American Control Conference (ACC)
language:
Short-container-title:

Author:

Wang Zifan¹,Shen Yi²,Zavlanos Michael M.²,Johansson Karl H.¹

Affiliation:

1. School of Electrical Enginnering and Computer Science, KTH Royal Institute of Technology, and also with Digital Futures,Division of Decision and Control Systems,Stockholm,Sweden,SE-10044

2. Duke University,Department of Mechanical Engineering and Materials Science,Durham,NC,USA

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10644130/10644150/10644891.pdf?arnumber=10644891

Reference24 articles.

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

2. No-regret learning in unknown games with correlated payoffs;Giuseppe Sessa;Advances in Neural Information Processing Systems,2019

3. Learning in games with continuous action sets and unknown payoff functions

4. Bandit learning in concave n-person games;Bravo;Advances in Neural Infor-mation Processing Systems,2018

5. Risk-Averse Control via CVaR Barrier Functions: Application to Bipedal Robot Locomotion

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