
The Advantage RegretMatching ActorCritic
Regret minimization has played a key role in online learning, equilibriu...
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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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Real World Games Look Like Spinning Tops
This paper investigates the geometrical properties of real world games (...
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The Automated Inspection of Opaque Liquid Vaccines
In the pharmaceutical industry the screening of opaque vaccines containi...
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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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Neural Replicator Dynamics
In multiagent learning, agents interact in inherently nonstationary envi...
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Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
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Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEAT
Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to...
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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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Fully Convolutional OneShot Object Segmentation for Industrial Robotics
The ability to identify and localize new objects robustly and effectivel...
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Robust temporal difference learning for critical domains
We present a new Qfunction operator for temporal difference (TD) learni...
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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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SCCrFMQ Learning in Cooperative Markov Games with Continuous Actions
Although many reinforcement learning methods have been proposed for lear...
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Negative Update Intervals in Deep MultiAgent Reinforcement Learning
In MultiAgent Reinforcement Learning, independent cooperative learners ...
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A Comparative Study of Bug Algorithms for Robot Navigation
This paper presents a literature survey and a comparative study of Bug A...
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Fast Convergence for Object Detection by Learning how to Combine Error Functions
In this paper, we introduce an innovative method to improve the converge...
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Reevaluating evaluation
Progress in machine learning is measured by careful evaluation on proble...
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Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that impro...
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Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
The ability of algorithms to evolve or learn (compositional) communicati...
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Emergent Communication through Negotiation
Multiagent reinforcement learning offers a way to study how communicati...
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Inequity aversion improves cooperation in intertemporal social dilemmas
Groups of humans are often able to find ways to cooperate with one anoth...
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Inequity aversion resolves intertemporal social dilemmas
Groups of humans are often able to find ways to cooperate with one anoth...
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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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SAIGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes
In multiagent environments, the capability of learning is important for ...
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The Mechanics of nPlayer Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
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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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Lenient MultiAgent Deep Reinforcement Learning
A significant amount of research in recent years has been dedicated towa...
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ValueDecomposition Networks For Cooperative MultiAgent Learning
We study the problem of cooperative multiagent reinforcement learning w...
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Karl Tuyls
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Staff Research Scientist at Google DeepMind since 2017, Professor of Computer Science at University of Liverpool since 2013, Visiting senior research fellow at King's College London from 20122014