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Decentralized Anti-coordination Through Multi-agent Learning

Publications associées (49)

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Improving Multi-agent Coordination by Learning to Estimate Contention

Boi Faltings, Panayiotis Danassis, Florian Wiedemair

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need ...

2021

Ageing and reinforcement learning

Michael Herzog, Wei-Hsiang Lin

Learning to achieve one’s goal in a complex environment is a complicated task. In reinforcement learning (RL) tasks, an agent interacts with the environment to learn optimal actions. In humans, striatal areas are strongly involved in these tasks. During ag ...

2021

Bandit Online Learning of Nash Equilibria in Monotone Games

Maryam Kamgarpour

We address online bandit learning of Nash equilibria in multi-agent convex games. We propose an algorithm whereby each agent uses only obtained values of her cost function at each joint played action, lacking any information of the functional form of her c ...

2021

Mixed Nash Equilibria in the Adversarial Examples Game

Rafaël Benjamin Pinot

This paper tackles the problem of adversarial examples from a game theoretic point of view. We study the open question of the existence of mixed Nash equilibria in the zero-sum game formed by the attacker and the classifier. While previous works usually al ...

2021

A Multiagent Model of Efficient and Sustainable Financial Markets

In this paper, we introduce a model of a financial market as a multiagent repeated game where the players are market makers. We formalize the concept of market making and the parameters of the game. Our main contribution is a framework that combines game t ...

2020

Learning Generalized Nash Equilibria in a Class of Convex Games

Maryam Kamgarpour

We consider multiagent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel dis ...

2019

Team Policy Learning For Multi-Agent Reinforcement Learning

Ali H. Sayed, Sulaiman A S A E Alghunaim, Lucas Cesar Eduardo Cassano

This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which K player ...

IEEE2019

Consistent Tomography Under Partial Observations Over Adaptive Networks

Ali H. Sayed

This paper studies the problem of inferring whether an agent is directly influenced by another agent over a network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update i ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Multi-agent reinforcement learning for adaptive demand response in smart cities

Jérôme Henri Kämpf

Buildings account for over 70% of the electricity use in the US. As cities grow, high peaks of electricity consumption are becoming more frequent, which leads to higher prices for electricity. Demand response is the coordination of electrical loads such th ...

IOP Publishing Ltd2019

No-Regret Learning in Unknown Games with Correlated Payoffs

Maryam Kamgarpour, Andreas Krause, Ilija Bogunovic

We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable i ...

Curran Associates, Inc.2019