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03/02/2023
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning
Progress in fields of machine learning and adversarial planning has bene...
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09/22/2022
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory Multi-Agent team at DeepMind studies several aspects...
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08/22/2022
Learning Correlated Equilibria in Mean-Field Games
The designs of many large-scale systems today, from traffic routing envi...
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06/30/2022
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
We introduce DeepNash, an autonomous agent capable of learning to play t...
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11/16/2021
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO
Recent advances in multiagent learning have seen the introduction ofa fa...
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10/22/2021
Solving N-player dynamic routing games with congestion: a mean field approach
The recent emergence of navigational tools has changed traffic patterns ...
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10/20/2021
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machi...
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09/20/2021
Generalization in Mean Field Games by Learning Master Policies
Mean Field Games (MFGs) can potentially scale multi-agent systems to ext...
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06/07/2021
Concave Utility Reinforcement Learning: the Mean-field Game viewpoint
Concave Utility Reinforcement Learning (CURL) extends RL from linear to ...
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05/17/2021
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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02/28/2021
Scaling up Mean Field Games with Online Mirror Descent
We address scaling up equilibrium computation in Mean Field Games (MFGs)...
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11/18/2020
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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08/27/2020
The Advantage Regret-Matching Actor-Critic
Regret minimization has played a key role in online learning, equilibriu...
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07/05/2020
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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06/08/2020
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Recent advances in deep reinforcement learning (RL) have led to consider...
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05/04/2020
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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02/19/2020
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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09/27/2019
A Generalized Training Approach for Multiagent Learning
This paper investigates a population-based training regime based on game...
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09/21/2019
Multiagent Evaluation under Incomplete Information
This paper investigates the evaluation of learned multiagent strategies ...
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08/26/2019
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in...
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07/04/2019
Approximate Fictitious Play for Mean Field Games
The theory of Mean Field Games (MFG) allows characterizing the Nash equi...
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06/24/2019
Foolproof Cooperative Learning
This paper extends the notion of equilibrium in game theory to learning ...
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06/01/2019
Neural Replicator Dynamics
In multiagent learning, agents interact in inherently nonstationary envi...
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03/13/2019
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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03/04/2019
α-Rank: Multi-Agent Evaluation by Evolution
We introduce α-Rank, a principled evolutionary dynamics methodology, for...
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01/23/2019
Open-ended Learning in Symmetric Zero-sum Games
Zero-sum games such as chess and poker are, abstractly, functions that e...
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12/17/2018
Malthusian Reinforcement Learning
Here we explore a new algorithmic framework for multi-agent reinforcemen...
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10/21/2018
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Optimization of parameterized policies for reinforcement learning (RL) i...
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09/20/2018
Playing the Game of Universal Adversarial Perturbations
We study the problem of learning classifiers robust to universal adversa...
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06/07/2018
Re-evaluating evaluation
Progress in machine learning is measured by careful evaluation on proble...
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03/16/2018
A Generalised Method for Empirical Game Theoretic Analysis
This paper provides theoretical bounds for empirical game theoretical an...
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11/14/2017
Symmetric Decomposition of Asymmetric Games
We introduce new theoretical insights into two-population asymmetric gam...
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11/02/2017
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
To achieve general intelligence, agents must learn how to interact with ...
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07/20/2017
