b'Karl Tuyls'

research

∙ 05/01/2023

Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas

In social psychology, Social Value Orientation (SVO) describes an indivi...

0 Udari Madhushani, et al. ∙

research

∙ 01/18/2023

Human-Timescale Adaptation in an Open-Ended Task Space

Foundation models have shown impressive adaptation and scalability in su...

0 Adaptive Agent Team, et al. ∙

research

∙ 01/11/2023

An Analysis of Quantile Temporal-Difference Learning

We analyse quantile temporal-difference learning (QTD), a distributional...

0 Mark Rowland, et al. ∙

research

∙ 10/17/2022

Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers

Solution concepts such as Nash Equilibria, Correlated Equilibria, and Co...

0 Luke Marris, 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

∙ 08/22/2022

Learning Correlated Equilibria in Mean-Field Games

The designs of many large-scale systems today, from traffic routing envi...

0 Paul Müller, 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

∙ 11/16/2021

Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO

Recent advances in multiagent learning have seen the introduction ofa fa...

0 Paul Müller, et al. ∙

research

∙ 10/21/2021

Statistical discrimination in learning agents

Undesired bias afflicts both human and algorithmic decision making, and ...

0 Edgar A. Duéñez-Guzmán, et al. ∙

research

∙ 06/28/2021

Evolutionary Dynamics and Φ-Regret Minimization in Games

Regret has been established as a foundational concept in online learning...

0 Georgios Piliouras, 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

∙ 06/08/2021

Time-series Imputation of Temporally-occluded Multiagent Trajectories

In multiagent environments, several decision-making individuals interact...

16 Shayegan Omidshafiei, et al. ∙

research

∙ 05/25/2021

From Motor Control to Team Play in Simulated Humanoid Football

Intelligent behaviour in the physical world exhibits structure at multip...

2 Siqi Liu, et al. ∙

research

∙ 02/28/2021

Scaling up Mean Field Games with Online Mirror Descent

We address scaling up equilibrium computation in Mean Field Games (MFGs)...

0 Julien Perolat, et al. ∙

research

∙ 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 ...

11 Karl Tuyls, 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

∙ 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 ...

11 Shayegan Omidshafiei, et al. ∙

research

∙ 04/20/2020

Real World Games Look Like Spinning Tops

This paper investigates the geometrical properties of real world games (...

10 Wojciech Marian Czarnecki, et al. ∙

research

∙ 02/21/2020

The Automated Inspection of Opaque Liquid Vaccines

In the pharmaceutical industry the screening of opaque vaccines containi...

9 Gregory Palmer, 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

∙ 09/21/2019

Multiagent Evaluation under Incomplete Information

This paper investigates the evaluation of learned multiagent strategies ...

24 Mark Rowland, 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

∙ 05/13/2019

Differentiable Game Mechanics

Deep learning is built on the foundational guarantee that gradient desce...

0 Alistair Letcher, et al. ∙

research

∙ 04/12/2019

Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEAT

Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to...

0 James Butterworth, 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

Fully Convolutional One-Shot Object Segmentation for Industrial Robotics

The ability to identify and localize new objects robustly and effectivel...

0 Benjamin Schnieders, et al. ∙

research

∙ 01/23/2019

Robust temporal difference learning for critical domains

We present a new Q-function operator for temporal difference (TD) learni...

20 Richard Klima, 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

∙ 09/18/2018

SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions

Although many reinforcement learning methods have been proposed for lear...

0 Chengwei Zhang, et al. ∙

research

∙ 09/13/2018

Negative Update Intervals in Deep Multi-Agent Reinforcement Learning

In Multi-Agent Reinforcement Learning, independent cooperative learners ...

0 Gregory Palmer, et al. ∙

research

∙ 08/15/2018

A Comparative Study of Bug Algorithms for Robot Navigation

This paper presents a literature survey and a comparative study of Bug A...

0 Kimberly McGuire, et al. ∙

research

∙ 08/13/2018

Fast Convergence for Object Detection by Learning how to Combine Error Functions

In this paper, we introduce an innovative method to improve the converge...

2 Benjamin Schnieders, et al. ∙

research

∙ 06/07/2018

Re-evaluating evaluation

Progress in machine learning is measured by careful evaluation on proble...

0 David Balduzzi, et al. ∙

research

∙ 06/05/2018

Relational Deep Reinforcement Learning

We introduce an approach for deep reinforcement learning (RL) that impro...

0 Vinicius Zambaldi, et al. ∙

research

∙ 04/11/2018

Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input

The ability of algorithms to evolve or learn (compositional) communicati...

0 Angeliki Lazaridou, 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

∙ 03/23/2018

Inequity aversion improves cooperation in intertemporal social dilemmas

Groups of humans are often able to find ways to cooperate with one anoth...

0 Edward Hughes, et al. ∙

research

∙ 03/23/2018

Inequity aversion resolves intertemporal social dilemmas

Groups of humans are often able to find ways to cooperate with one anoth...

0 Edward Hughes, 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

∙ 03/08/2018

SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes

In multiagent environments, the capability of learning is important for ...

0 Chengwei Zhang, et al. ∙

research

∙ 02/15/2018

The Mechanics of n-Player Differentiable Games

The cornerstone underpinning deep learning is the guarantee that gradien...

0 David Balduzzi, 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

∙ 07/20/2017

A multi-agent reinforcement learning model of common-pool resource appropriation

Humanity faces numerous problems of common-pool resource appropriation. ...

0 Julien Perolat, et al. ∙

research

∙ 07/14/2017

Lenient Multi-Agent Deep Reinforcement Learning

A significant amount of research in recent years has been dedicated towa...

0 Gregory Palmer, 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. ∙

Karl Tuyls

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