Luke Marris

According to our database¹, Luke Marris authored at least 23 papers between 2018 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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Bibliography

2024

Convex Markov Games: A Framework for Fairness, Imitation, and Creativity in Multi-Agent Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Visualizing 2x2 Normal-Form Games: twoxtwogame LaTeX Package.

[BibT_eX]

[DOI]

CoRR, 2024

States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers.

[BibT_eX]

[DOI]

CoRR, 2024

Generative Adversarial Equilibrium Solvers.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization.

[BibT_eX]

[DOI]

Ian Gemp

Luke Marris

Georgios Piliouras

Proceedings of the Twelfth International Conference on Learning Representations, 2024

NfgTransformer: Equivariant Representation Learning for Normal-form Games.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Neural Population Learning beyond Symmetric Zero-Sum Games.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

Approximating the Core via Iterative Coalition Sampling.

[BibT_eX]

[DOI]

Edgar A. Duéñez-Guzmán

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

2023

Evaluating Agents using Social Choice Theory.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

Luke Marris

Ian Gemp

Georgios Piliouras

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Search-Improved Game-Theoretic Multiagent Reinforcement Learning in General and Negotiation Games.

[BibT_eX]

[DOI]

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Figure Data for the paper "From Motor Control to Team Play in Simulated Humanoid Football".

[BibT_eX]

[DOI]

Dataset, August, 2022

From motor control to team play in simulated humanoid football.

[BibT_eX]

[DOI]

Sci. Robotics, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Developing, evaluating and scaling learning agents in multi-agent environments.

[BibT_eX]

[DOI]

AI Commun., 2022

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

NeuPL: Neural Population Learning.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

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

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

A Generalized Training Approach for Multiagent Learning.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.

[BibT_eX]

[DOI]

Max Jaderberg

Wojciech M. Czarnecki

Iain Dunning

Luke Marris

Guy Lever

Antonio García Castañeda

CoRR, 2018

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Luke Marris

Timeline

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