Rob Powers

Trond Grenager

Yoav Shoham

English

Research Papers in Economics

A decisiontheoretic generalization of on-line learning and an application to boosting.

A generalized reinforcement-learning model: Convergence and applications.

A near-optimal polynomial time algorithm for learning in certain classes of stochastic games.

A simple adaptive procedure leading to correlated equilibrium.

An iterative method of solving a game.

Approximating game-theoretic optimal strategies for full-scale poker.

Approximation to Bayes risk in repeated plays. Contributions to the Theory of Games,

Controlled random walks.

Convergence and no-regret in multiagent learning.

Correlated Qlearning.

Dynamic Programming.

Efﬁcient learning equilibrium.

Eﬃcient no-regret multiagent learning.

Evolution and the Theory of Games.

Evolutionary selection dynamics in games: Convergence and limit properties.

Friend-or-foe Q-learning in general-sum games.

Iterative solution of games by ﬁctitious play. In Activity Analysis of Production and Allocation.

Learning against multiple opponents.

Learning against opponents with bounded memory.

Learning to coordinate without sharing information.

Learning to play games in extensive form by valuation.

Local-eﬀect games.

Machine Learning.

Markov games as a framework for multiagent reinforcement learning.

Mobilized ad-hoc networks: A reinforcement learning approach.

Multiagent planning with factored mdps.

Multiagent reinforcement learning: Theoretical framework and an algorithm.

Nash Q-learning for general-sum stochastic games.

Near-optimal reinforcement learning in polynomial time.

New criteria and a new algorithm for learning in multi-agent systems.

On no-regret learning, ﬁctitious play, and Nash equilibrium.

On the agenda(s) of research on multi-agent learning.

On the convergence of learning processes in a 2x2 non-zero-person game.

Online convex programming and generalized inﬁnitesimal gradient ascent.

Predicting how people play games: reinforcement leaning in experimental games with unique, mixed strategy equilibria.

Rational learning leads to Nash equilibrium.

Rationality of self and others in an economic system.

Regret in the on-line decision problem.

Reinforcement learning of coordination in heterogeneous cooperative multiagent systems.

Reinforcement learning to play an optimal Nash equilibrium in team markov games.

Reinforcement learning: A survey.

Reinforcement Learning: An Introduction.

Replicator dynamics.

Representations and solutions for game-theoretic problems. Artiﬁcial Intelligence, 94(1):167–215. 19[Lauer and Riedmiller,

Run the GAMUT: A comprehensive approach to evaluating game-theoretic algorithms.

Sophisticated EWA learning and strategic teaching in repeated games.

Strategic Learning and Its Limits.

The dynamics of reinforcement learning in cooperative multiagent systems.

The empirical bayes envelope and regret minimization in competitive markov decision processes.

Universal consistency and cautious ﬁctitious play.

Walverine: A walrasian trading agent. Decision Support Systems,

If multi-agent learning is the answer, what is the question?

http://www.dklevine.com/archive/refs4122247000000001156.pdf

If multi-agent learning is the answer, what is the question?

Abstract

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Research Papers in Economics