693 citations · 739 across the 24 of their papers we have counts for
24 papers
Paradoxes of Game Theoretic Equilibria and Price of Anarchy
Georgios Piliouras, Ian Gemp, Siqi Liu +1
For decades, static solution concepts (Nash, Correlated, and Coarse Correlated Equilibria) and the Price of Anarchy (PoA) have formed the bedrock of algorithmic game theory, with n…
Deep Incentive Design with Differentiable Equilibrium Blocks
Vinzenz Thoma, Georgios Piliouras, Luke Marris
Automated design of multi-agent interactions with desirable equilibrium outcomes is inherently difficult due to the computational hardness, non-uniqueness, and instability of the r…
Deviation Ratings: A General, Clone-Invariant Rating Method
Luke Marris, Siqi Liu, Ian Gemp +2
Many real-world multi-agent or multi-task evaluation scenarios can be naturally modelled as normal-form games due to inherent strategic (adversarial, cooperative, and mixed motive)…
Re-evaluating Open-ended Evaluation of Large Language Models
Siqi Liu, Ian Gemp, Luke Marris +3
Evaluation has traditionally focused on ranking candidates for a specific skill. Modern generalist models, such as Large Language Models (LLMs), decidedly outpace this paradigm. Op…
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
Ian Gemp, Andreas Haupt, Luke Marris +2
Behavioral diversity, expert imitation, fairness, safety goals and others give rise to preferences in sequential decision making domains that do not decompose additively across tim…
Visualizing 2x2 Normal-Form Games: twoxtwogame LaTeX Package
Luke Marris, Ian Gemp, Siqi Liu +2
Normal-form games with two players, each with two strategies, are the most studied class of games. These so-called 2x2 games are used to model a variety of strategic interactions.…
NfgTransformer: Equivariant Representation Learning for Normal-form Games
Siqi Liu, Luke Marris, Georgios Piliouras +2
Normal-form games (NFGs) are the fundamental model of strategic interaction. We study their representation using neural networks. We describe the inherent equivariance of NFGs -- a…
Approximating the Core via Iterative Coalition Sampling
Ian Gemp, Marc Lanctot, Luke Marris +11
The core is a central solution concept in cooperative game theory, defined as the set of feasible allocations or payments such that no subset of agents has incentive to break away…
Steering Language Models with Game-Theoretic Solvers
Ian Gemp, Roma Patel, Yoram Bachrach +6
Mathematical models of interactions among rational agents have long been studied in game theory. However these interactions are often over a small set of discrete game actions whic…
Neural Population Learning beyond Symmetric Zero-sum Games
Siqi Liu, Luke Marris, Marc Lanctot +3
We study computationally efficient methods for finding equilibria in n-player general-sum games, specifically ones that afford complex visuomotor skills. We show how existing metho…
Evaluating Agents using Social Choice Theory
Marc Lanctot, Kate Larson, Yoram Bachrach +6
We argue that many general evaluation problems can be viewed through the lens of voting theory. Each task is interpreted as a separate voter, which requires only ordinal rankings o…
Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization
Ian Gemp, Luke Marris, Georgios Piliouras
We propose the first loss function for approximate Nash equilibria of normal-form games that is amenable to unbiased Monte Carlo estimation. This construction allows us to deploy s…
Equilibrium-Invariant Embedding, Metric Space, and Fundamental Set of Normal-Form Games
Luke Marris, Ian Gemp, Georgios Piliouras
Equilibrium solution concepts of normal-form games, such as Nash equilibria, correlated equilibria, and coarse correlated equilibria, describe the joint strategy profiles from whic…
Generative Adversarial Equilibrium Solvers
Denizalp Goktas, David C. Parkes, Ian Gemp +5
We introduce the use of generative adversarial learning to compute equilibria in general game-theoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo-game…
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Zun Li, Marc Lanctot, Kevin R. McKee +7
Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…
Game Theoretic Rating in N-player general-sum games with Equilibria
Luke Marris, Marc Lanctot, Ian Gemp +5
Rating strategies in a game is an important area of research in game theory and artificial intelligence, and can be applied to any real-world competitive or cooperative setting. Tr…
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers
Luke Marris, Ian Gemp, Thomas Anthony +3
Solution concepts such as Nash Equilibria, Correlated Equilibria, and Coarse Correlated Equilibria are useful components for many multiagent machine learning algorithms. Unfortunat…
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Siqi Liu, Marc Lanctot, Luke Marris +1
Learning to play optimally against any mixture over a diverse set of strategies is of important practical interests in competitive games. In this paper, we propose simplex-NeuPL th…
NeuPL: Neural Population Learning
Siqi Liu, Luke Marris, Daniel Hennes +3
Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, gr…
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Luke Marris, Paul Muller, Marc Lanctot +2
Two-player, constant-sum games are well studied in the literature, but there has been limited progress outside of this setting. We propose Joint Policy-Space Response Oracles (JPSR…
From Motor Control to Team Play in Simulated Humanoid Football
Siqi Liu, Guy Lever, Zhe Wang +19
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous mus…
A Generalized Training Approach for Multiagent Learning
Paul Muller, Shayegan Omidshafiei, Mark Rowland +12
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense that it (1)…
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning +15
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based g…