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Last round convergence and no-dynamic regret in asymmetric repeated games

Last round convergence and no-dynamic regret in asymmetric repeated games
Last round convergence and no-dynamic regret in asymmetric repeated games
This paper considers repeated games in which one player has a different objective than others. In particular, we investigate repeated two-player zero-sum games where the column player not only aims to minimize her regret but also stabilize the actions. Suppose that while repeatedly playing this game, the row player chooses her strategy at each round by using a no-regret algorithm to minimize her regret. We develop a no-dynamic regret algorithm for the column player to exhibit last round convergence to a minimax equilibrium. We show that our algorithm is efficient against a large set of popular no-regret algorithms the row player can use, including the multiplicative weights update algorithm, general follow-the-regularized-leader and any no-regret algorithms satisfy a property so called “stability”.
553-577
PMLR
Dinh, Le Cong
e89b4443-9eff-4790-b101-9eabe5ef947c
Nguyen, Tri-Dung
a6aa7081-6bf7-488a-b72f-510328958a8e
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Tran-Thanh, Long
633282bf-f7ff-4137-ada6-6d4f19262676
Feldman, Vitaly
Ligett, Katrina
Sabato, Sivan
Dinh, Le Cong
e89b4443-9eff-4790-b101-9eabe5ef947c
Nguyen, Tri-Dung
a6aa7081-6bf7-488a-b72f-510328958a8e
Zemkoho, Alain B.
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Tran-Thanh, Long
633282bf-f7ff-4137-ada6-6d4f19262676
Feldman, Vitaly
Ligett, Katrina
Sabato, Sivan

Dinh, Le Cong, Nguyen, Tri-Dung, Zemkoho, Alain B. and Tran-Thanh, Long (2021) Last round convergence and no-dynamic regret in asymmetric repeated games. Feldman, Vitaly, Ligett, Katrina and Sabato, Sivan (eds.) In Proceedings of the 32nd International Conference on Algorithmic Learning Theory. vol. 132, PMLR. pp. 553-577 .

Record type: Conference or Workshop Item (Paper)

Abstract

This paper considers repeated games in which one player has a different objective than others. In particular, we investigate repeated two-player zero-sum games where the column player not only aims to minimize her regret but also stabilize the actions. Suppose that while repeatedly playing this game, the row player chooses her strategy at each round by using a no-regret algorithm to minimize her regret. We develop a no-dynamic regret algorithm for the column player to exhibit last round convergence to a minimax equilibrium. We show that our algorithm is efficient against a large set of popular no-regret algorithms the row player can use, including the multiplicative weights update algorithm, general follow-the-regularized-leader and any no-regret algorithms satisfy a property so called “stability”.

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More information

Accepted/In Press date: 21 December 2020
e-pub ahead of print date: 1 April 2021
Published date: 1 April 2021

Identifiers

Local EPrints ID: 448061
URI: http://eprints.soton.ac.uk/id/eprint/448061
PURE UUID: 46435bc8-66f5-43df-a6b5-0802bbf3fda3
ORCID for Le Cong Dinh: ORCID iD orcid.org/0000-0002-3306-0603
ORCID for Tri-Dung Nguyen: ORCID iD orcid.org/0000-0002-4158-9099
ORCID for Alain B. Zemkoho: ORCID iD orcid.org/0000-0003-1265-4178

Catalogue record

Date deposited: 01 Apr 2021 15:41
Last modified: 17 Mar 2024 03:37

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Contributors

Author: Le Cong Dinh ORCID iD
Author: Tri-Dung Nguyen ORCID iD
Author: Long Tran-Thanh
Editor: Vitaly Feldman
Editor: Katrina Ligett
Editor: Sivan Sabato

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