Decentralized Learning in Stochastic Games with Local Information

In the context of multi-agent systems with decentralized information structures, we study rigorously justified convergence results and associated learning algorithms that converge to equilibria. With this objective in mind, we first review classical equilibrium results, focusing on finite-player games with pure or mixed strategy sets. Results such as Kakutani’s fixed-point theorem and Sion’s minimax theorem establish existence under relatively broad conditions. Building on this background, we then study learning dynamics, including best and better response processes, in which players periodically revise and update strategies to optimize payoffs relative to their previous actions via a policy revision process. This induces a graph on the set of policies which facilitate our mathematical approach which combines graph theory, game theory, stochastic control, and Markov processes. While learning using best/better response dynamics converges under certain conditions reported in Arslan et.al, a new approach to policy revision, termed as satisficing (which may be viewed as a win-stay, lose-shift algorithm), introduced by Yongacoglu et.al provides a strictly richer graph network structure and is applicable to a much broader class of games. In particular, these generalize weakly acyclic games. The question we studied is to precisely characterize the set of games for which such a satisficing process ensures convergence to equilibrium. In particular, we addressed an open question raised by Yongacoglu et al. on necessary and sufficient conditions for convergence to equilibria from any initial policy profile. On sufficiency, we presented a generalization, relaxing requirements to allow multiple pure Nash equilibria, provided at least one is strict and subgame-unique. Our research also presented a nontrivial example of a game that admits a strict pure Nash equilibrium in each induced subgame that fails to converge via satisficing paths, showing that such conditions are insufficient, thus also leading to a necessity condition.

Authors

Institutions

Publication Details

Journal
Inquiry Queen s Undergraduate Research Conference Proceedings
Published
2025-08-29
DOI
https://doi.org/10.24908/iqurcp19805
Primary Topic
Distributed Sensor Networks and Detection Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decentralized Learning in Stochastic Games with Local Information

Gregory S. Hickey
Inquiry Queen s Undergraduate Research Conference Proceedings
Distributed Sensor Networks and Detection Algorithms
article

Decentralized Learning in Stochastic Games with Local Information

Gregory S. Hickey
article en

Abstract

In the context of multi-agent systems with decentralized information structures, we study rigorously justified convergence results and associated learning algorithms that converge to equilibria. With this objective in mind, we first review classical equilibrium results, focusing on finite-player games with pure or mixed strategy sets. Results such as Kakutani’s fixed-point theorem and Sion’s minimax theorem establish existence under relatively broad conditions. Building on this background, we then study learning dynamics, including best and better response processes, in which players periodically revise and update strategies to optimize payoffs relative to their previous actions via a policy revision process. This induces a graph on the set of policies which facilitate our mathematical approach which combines graph theory, game theory, stochastic control, and Markov processes. While learning using best/better response dynamics converges under certain conditions reported in Arslan et.al, a new approach to policy revision, termed as satisficing (which may be viewed as a win-stay, lose-shift algorithm), introduced by Yongacoglu et.al provides a strictly richer graph network structure and is applicable to a much broader class of games. In particular, these generalize weakly acyclic games. The question we studied is to precisely characterize the set of games for which such a satisficing process ensures convergence to equilibrium. In particular, we addressed an open question raised by Yongacoglu et al. on necessary and sufficient conditions for convergence to equilibria from any initial policy profile. On sufficiency, we presented a generalization, relaxing requirements to allow multiple pure Nash equilibria, provided at least one is strict and subgame-unique. Our research also presented a nontrivial example of a game that admits a strict pure Nash equilibrium in each induced subgame that fails to converge via satisficing paths, showing that such conditions are insufficient, thus also leading to a necessity condition.

Inquiry Queen s Undergraduate Research Conference ProceedingsVol. 19(2)
Queen's University (CA)
Openalex Percentile: Top 43%
Distributed Sensor Networks and Detection Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Decentralized Learning in Stochastic Games with Local Information — Gregory S. Hickey · Inquiry Queen s Undergraduate Research Conference Proceedings (2025) | TGRS Research Map | TGRS