Ian Gemp | DeepAI

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∙ 04/19/2023

Equilibrium-Invariant Embedding, Metric Space, and Fundamental Set of 2×2 Normal-Form Games

Equilibrium solution concepts of normal-form games, such as Nash equilib...

0 Luke Marris, et al. ∙

∙ 02/13/2023

Generative Adversarial Equilibrium Solvers

We introduce the use of generative adversarial learning to compute equil...

0 Denizalp Goktas, et al. ∙

∙ 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. ∙

∙ 11/17/2022

AlphaSnake: Policy Iteration on a Nondeterministic NP-hard Markov Decision Process

Reinforcement learning has recently been used to approach well-known NP-...

0 Kevin Du, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 07/29/2022

Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering

Large graphs commonly appear in social networks, knowledge graphs, recom...

0 Elise van der Pol, et al. ∙

∙ 06/10/2022

The Generalized Eigenvalue Problem as a Nash Equilibrium

The generalized eigenvalue problem (GEP) is a fundamental concept in num...

0 Ian Gemp, et al. ∙

∙ 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. ∙

∙ 02/08/2021

EigenGame Unloaded: When playing games is better than optimizing

We build on the recently proposed EigenGame that views eigendecompositio...

0 Ian Gemp, et al. ∙

∙ 10/01/2020

D3C: Reducing the Price of Anarchy in Multi-Agent Learning

Even in simple multi-agent systems, fixed incentives can lead to outcome...

0 Ian Gemp, et al. ∙

∙ 10/01/2020

EigenGame: PCA as a Nash Equilibrium

We present a novel view on principal component analysis (PCA) as a compe...

6 Ian Gemp, et al. ∙

∙ 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. ∙

∙ 06/06/2020

Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity

In this paper, we introduce proximal gradient temporal difference learni...

0 Bo Liu, et al. ∙

∙ 02/06/2020

Social Diversity and Social Preferences in Mixed-Motive Reinforcement Learning

Recent research on reinforcement learning in pure-conflict and pure-comm...

4 Kevin R. McKee, et al. ∙

∙ 08/04/2018

Global Convergence to the Equilibrium of GANs using Variational Inequalities

In optimization, the negative gradient of a function denotes the directi...

0 Ian Gemp, et al. ∙

∙ 10/19/2017

Online Monotone Games

Algorithmic game theory (AGT) focuses on the design and analysis of algo...

0 Ian Gemp, et al. ∙

∙ 11/05/2016

Generative Multi-Adversarial Networks

Generative adversarial networks (GANs) are a framework for producing a g...

0 Ishan Durugkar, et al. ∙

∙ 08/21/2016

Inverting Variational Autoencoders for Improved Generative Accuracy

Recent advances in semi-supervised learning with deep generative models ...

0 Ian Gemp, et al. ∙

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