
Concave Utility Reinforcement Learning: the Meanfield Game viewpoint
Concave Utility Reinforcement Learning (CURL) extends RL from linear to ...
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Mean Field Games Flock! The Reinforcement Learning Way
We present a method enabling a large number of agents to learn how to fl...
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Scaling up Mean Field Games with Online Mirror Descent
We address scaling up equilibrium computation in Mean Field Games (MFGs)...
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Game Plan: What AI can do for Football, and What Football can do for AI
The rapid progress in artificial intelligence (AI) and machine learning ...
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The Advantage RegretMatching ActorCritic
Regret minimization has played a key role in online learning, equilibriu...
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Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
In this paper, we deepen the analysis of continuous time Fictitious Play...
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Learning to Play NoPress Diplomacy with Best Response Policy Iteration
Recent advances in deep reinforcement learning (RL) have led to consider...
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Navigating the Landscape of Multiplayer Games to Probe the Drosophila of AI
Multiplayer games have a long history in being used as key testbeds for ...
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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
In this paper we investigate the Follow the Regularized Leader dynamics ...
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A Generalized Training Approach for Multiagent Learning
This paper investigates a populationbased training regime based on game...
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Multiagent Evaluation under Incomplete Information
This paper investigates the evaluation of learned multiagent strategies ...
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OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in...
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Approximate Fictitious Play for Mean Field Games
The theory of Mean Field Games (MFG) allows characterizing the Nash equi...
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Foolproof Cooperative Learning
This paper extends the notion of equilibrium in game theory to learning ...
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Neural Replicator Dynamics
In multiagent learning, agents interact in inherently nonstationary envi...
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Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
In this paper, we present exploitability descent, a new algorithm to com...
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αRank: MultiAgent Evaluation by Evolution
We introduce αRank, a principled evolutionary dynamics methodology, for...
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Openended Learning in Symmetric Zerosum Games
Zerosum games such as chess and poker are, abstractly, functions that e...
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Malthusian Reinforcement Learning
Here we explore a new algorithmic framework for multiagent reinforcemen...
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ActorCritic Policy Optimization in Partially Observable Multiagent Environments
Optimization of parameterized policies for reinforcement learning (RL) i...
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Playing the Game of Universal Adversarial Perturbations
We study the problem of learning classifiers robust to universal adversa...
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Reevaluating evaluation
Progress in machine learning is measured by careful evaluation on proble...
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A Generalised Method for Empirical Game Theoretic Analysis
This paper provides theoretical bounds for empirical game theoretical an...
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Symmetric Decomposition of Asymmetric Games
We introduce new theoretical insights into twopopulation asymmetric gam...
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A Unified GameTheoretic Approach to Multiagent Reinforcement Learning
To achieve general intelligence, agents must learn how to interact with ...
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A multiagent reinforcement learning model of commonpool resource appropriation
Humanity faces numerous problems of commonpool resource appropriation. ...
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Julien Perolat
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