b'Marc Lanctot'

research

∙ 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...

0 Marc Lanctot, et al. ∙

research

∙ 03/02/2023

Learning not to Regret

Regret minimization is a key component of many algorithms for finding Na...

0 David Sychrovský, et al. ∙

research

∙ 02/01/2023

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

Multiagent reinforcement learning (MARL) has benefited significantly fro...

0 Zun Li, et al. ∙

research

∙ 10/05/2022

Game Theoretic Rating in N-player general-sum games with Equilibria

Rating strategies in a game is an important area of research in game the...

10 Luke Marris, et al. ∙

research

∙ 09/22/2022

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

The Game Theory Multi-Agent team at DeepMind studies several aspects...

3 Ian Gemp, et al. ∙

research

∙ 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...

6 Julien Perolat, et al. ∙

research

∙ 06/12/2022

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

Algorithms designed for single-agent reinforcement learning (RL) general...

14 Samuel Sokota, et al. ∙

research

∙ 06/08/2022

ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret

Recent techniques for approximating Nash equilibria in very large games ...

5 Stephen McAleer, et al. ∙

research

∙ 05/31/2022

Simplex NeuPL: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

Learning to play optimally against any mixture over a diverse set of str...

0 Siqi Liu, et al. ∙

research

∙ 05/24/2022

Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections

Hindsight rationality is an approach to playing general-sum games that p...

5 Dustin Morrill, et al. ∙

research

∙ 01/19/2022

Anytime PSRO for Two-Player Zero-Sum Games

Policy space response oracles (PSRO) is a multi-agent reinforcement lear...

1 Stephen McAleer, et al. ∙

research

∙ 12/06/2021

Player of Games

Games have a long history of serving as a benchmark for progress in arti...

0 Martin Schmid, et al. ∙

research

∙ 06/17/2021

Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

Two-player, constant-sum games are well studied in the literature, but t...

0 Luke Marris, et al. ∙

research

∙ 06/02/2021

Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent

Nash equilibrium is a central concept in game theory. Several Nash solve...

0 Ian Gemp, et al. ∙

research

∙ 02/13/2021

Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games

Hindsight rationality is an approach to playing multi-agent, general-sum...

4 Dustin Morrill, et al. ∙

research

∙ 01/11/2021

Solving Common-Payoff Games with Approximate Policy Iteration

For artificially intelligent learning systems to have widespread applica...

0 Samuel Sokota, et al. ∙

research

∙ 12/10/2020

Hindsight and Sequential Rationality of Correlated Play

Driven by recent successes in two-player, zero-sum game solving and play...

1 Dustin Morrill, et al. ∙

research

∙ 10/20/2020

Negotiating Team Formation Using Deep Reinforcement Learning

When autonomous agents interact in the same environment, they must often...

0 Yoram Bachrach, et al. ∙

research

∙ 08/27/2020

The Advantage Regret-Matching Actor-Critic

Regret minimization has played a key role in online learning, equilibriu...

0 Audrūnas Gruslys, et al. ∙

research

∙ 06/15/2020

Sound Search in Imperfect Information Games

Search has played a fundamental role in computer game research since the...

0 Michal Šustr, et al. ∙

research

∙ 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...

0 Thomas Anthony, et al. ∙

research

∙ 04/20/2020

Approximate exploitability: Learning a best response in large games

A common metric in games of imperfect information is exploitability, i.e...

10 Finbarr Timbers, et al. ∙

research

∙ 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 ...

32 Julien Perolat, et al. ∙

research

∙ 09/27/2019

A Generalized Training Approach for Multiagent Learning

This paper investigates a population-based training regime based on game...

20 Paul Müller, et al. ∙

research

∙ 08/26/2019

OpenSpiel: A Framework for Reinforcement Learning in Games

OpenSpiel is a collection of environments and algorithms for research in...

12 Marc Lanctot, et al. ∙

research

∙ 06/01/2019

Neural Replicator Dynamics

In multiagent learning, agents interact in inherently nonstationary envi...

12 Shayegan Omidshafiei, et al. ∙

research

∙ 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...

20 Edward Lockhart, et al. ∙

research

∙ 03/04/2019

α-Rank: Multi-Agent Evaluation by Evolution

We introduce α-Rank, a principled evolutionary dynamics methodology, for...

0 Shayegan Omidshafiei, et al. ∙

research

∙ 03/02/2019

Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research

Evolution has produced a multi-scale mosaic of interacting adaptive unit...

16 Joel Z. Leibo, et al. ∙

research

∙ 02/01/2019

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...

54 Nolan Bard, et al. ∙

research

∙ 10/21/2018

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Optimization of parameterized policies for reinforcement learning (RL) i...

8 Sriram Srinivasan, et al. ∙

research

∙ 09/09/2018

Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines

Learning strategies for imperfect information games from samples of inte...

12 Martin Schmid, et al. ∙

research

∙ 04/11/2018

Emergent Communication through Negotiation

Multi-agent reinforcement learning offers a way to study how communicati...

0 Kris Cao, et al. ∙

research

∙ 03/16/2018

A Generalised Method for Empirical Game Theoretic Analysis

This paper provides theoretical bounds for empirical game theoretical an...

0 Karl Tuyls, et al. ∙

research

∙ 12/05/2017

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

The game of chess is the most widely-studied domain in the history of ar...

0 David Silver, et al. ∙

research

∙ 11/14/2017

Symmetric Decomposition of Asymmetric Games

We introduce new theoretical insights into two-population asymmetric gam...

0 Karl Tuyls, et al. ∙

research

∙ 11/02/2017

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

To achieve general intelligence, agents must learn how to interact with ...

0 Marc Lanctot, et al. ∙

research

∙ 06/16/2017

Value-Decomposition Networks For Cooperative Multi-Agent Learning

We study the problem of cooperative multi-agent reinforcement learning w...

0 Peter Sunehag, et al. ∙

research

∙ 04/12/2017

Deep Q-learning from Demonstrations

Deep reinforcement learning (RL) has achieved several high profile succe...

0 Todd Hester, et al. ∙

research

∙ 02/10/2017

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Matrix games like Prisoner's Dilemma have guided research on social dile...

0 Joel Z. Leibo, et al. ∙

research

∙ 06/10/2016

Memory-Efficient Backpropagation Through Time

We propose a novel approach to reduce memory consumption of the backprop...

0 Audrūnas Gruslys, et al. ∙

research

∙ 06/08/2016

Convolution by Evolution: Differentiable Pattern Producing Networks

In this work we introduce a differentiable version of the Compositional ...

0 Chrisantha Fernando, et al. ∙

research

∙ 06/02/2014

Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups

Monte Carlo Tree Search (MCTS) has improved the performance of game engi...

0 Marc Lanctot, et al. ∙

research

∙ 05/03/2012

No-Regret Learning in Extensive-Form Games with Imperfect Recall

Counterfactual Regret Minimization (CFR) is an efficient no-regret learn...

0 Marc Lanctot, et al. ∙

Marc Lanctot

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