THEORETICAL PRINCIPLES OF MULTI-AGENT REINFORCEMENT LEARNING FOR COALITIONAL BARGAINING GAMES

Lucia Cipolina-Kun

THEORETICAL PRINCIPLES OF MULTI-AGENT REINFORCEMENT LEARNING FOR COALITIONAL BARGAINING GAMES

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Abstract

The rising focus on employing multi-agent reinforcement learning (MARL) in coalition bargaining games (CBG) has exposed a need for robust theoretical principles linking the two. To address this, we explore the relationship between CBG and MARL within the context of stochastic games, and show that under some
assumptions, CBG are a subclass of sequential stochastic games. Out work is a step forward in the reproducibility and generalization of MARL results to CBG.

Original language	English
Publication status	Accepted/In press - 31 May 2023
Event	International Conference on Learning Representations - Kigali Convention Center. Rwanda, Kigali, Rwanda Duration: 1 May 2023 → 5 May 2023 Conference number: 11 https://iclr.cc/

Conference

Conference	International Conference on Learning Representations
Abbreviated title	ICLR
Country/Territory	Rwanda
City	Kigali
Period	1/05/23 → 5/05/23
Internet address	https://iclr.cc/

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title = "THEORETICAL PRINCIPLES OF MULTI-AGENT REINFORCEMENT LEARNING FOR COALITIONAL BARGAINING GAMES",

abstract = "The rising focus on employing multi-agent reinforcement learning (MARL) in coalition bargaining games (CBG) has exposed a need for robust theoretical principles linking the two. To address this, we explore the relationship between CBG and MARL within the context of stochastic games, and show that under someassumptions, CBG are a subclass of sequential stochastic games. Out work is a step forward in the reproducibility and generalization of MARL results to CBG.",

author = "Lucia Cipolina-Kun",

year = "2023",

month = may,

day = "31",

language = "English",

note = "International Conference on Learning Representations , ICLR ; Conference date: 01-05-2023 Through 05-05-2023",

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AU - Cipolina-Kun, Lucia

N1 - Conference code: 11

PY - 2023/5/31

Y1 - 2023/5/31

N2 - The rising focus on employing multi-agent reinforcement learning (MARL) in coalition bargaining games (CBG) has exposed a need for robust theoretical principles linking the two. To address this, we explore the relationship between CBG and MARL within the context of stochastic games, and show that under someassumptions, CBG are a subclass of sequential stochastic games. Out work is a step forward in the reproducibility and generalization of MARL results to CBG.

AB - The rising focus on employing multi-agent reinforcement learning (MARL) in coalition bargaining games (CBG) has exposed a need for robust theoretical principles linking the two. To address this, we explore the relationship between CBG and MARL within the context of stochastic games, and show that under someassumptions, CBG are a subclass of sequential stochastic games. Out work is a step forward in the reproducibility and generalization of MARL results to CBG.

UR - https://openreview.net/pdf?id=OaZktJBVpUy

M3 - Conference Abstract

T2 - International Conference on Learning Representations

Y2 - 1 May 2023 through 5 May 2023

ER -

THEORETICAL PRINCIPLES OF MULTI-AGENT REINFORCEMENT LEARNING FOR COALITIONAL BARGAINING GAMES

Abstract

Conference

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