Sep 1, 2023 · Our approach is based on an adaptive Online Mirror Descent (OMD) algorithm that applies OMD locally to each information set, using individually ...
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Nov 6, 2024 · The paper studies the extensive-form game under the fixed sampling policy framework. It proposes the algorithm based on online mirror descent. The paper gives ...
Jan 25, 2024 · We study how to learn ε-optimal strategies in zero-sum imperfect information games (IIG) with trajectory feedback.
Sep 1, 2023 · Our approach is based on an adaptive Online Mirror Descent (OMD) algorithm that applies OMD locally to each information set, using individually ...
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What is the difference between an extensive form game and a simultaneous move game?
Local and adaptive mirror descents in extensive-form games ... We study how to learn ϵ -optimal strategies in zero-sum imperfect information games (IIG) with ...
Abstract—Online Mirror Descent (OMD) is an important and widely used class of adaptive learning algorithms that enjoys good regret performance guarantees.
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Local and adaptive mirror descents in extensive-form games ... We study how to learn ϵ -optimal strategies in zero-sum imperfect information games (IIG) with ...
The second is to use Online Mirror Descent (OMD) algorithms via suitably designed regularizers over the parameter space. This approach has been successfully.
Faster Optimistic Online Mirror Descent for Extensive-Form Games ... adaptive method is presented to speed up the optimistic variants of OMD. ... game offline, then ...
This paper resolves the open question of designing near-optimal algorithms for learn- ing imperfect-information extensive-form games from bandit feedback.