Richard Everett

According to our database, Richard Everett authored at least 20 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

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

Udari Madhushani, Kevin R. McKee, John P. Agapiou, Joel Z. Leibo, Richard Everett, Thomas W. Anthony, Edward Hughes, Karl Tuyls, Edgar A. Duéñez-Guzmán

CoRR, 2023

2022

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments.

Ian Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

CoRR, 2022

Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering.

Elise van der Pol, Ian Gemp, Yoram Bachrach, Richard Everett

CoRR, 2022

Learning Robust Real-Time Cultural Transmission without Human Data.

Avishkar Bhoopchand, Bethanie Brownfield, Adrian Collister, Agustin Dal Lago, Ashley Edwards, Richard Everett, Alexandre Fréchette, Yanko Gitahy Oliveira, Edward Hughes, Kory W. Mathewson, Piermaria Mendolicchio, Julia Pawar, Miruna Pislar, Alex Platonov, Evan Senter, Sukhdeep Singh, Alexander Zacherl, Lei M. Zhang

CoRR, 2022

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria.

Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas, Alexander Sasha Vezhnevets, John P. Agapiou, Kevin R. McKee, Richard Everett, Janusz Marecki, Joel Z. Leibo, Thore Graepel

CoRR, 2022

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

Ian Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

AI Commun., 2022

Quantifying the effects of environment and population diversity in multi-agent reinforcement learning.

Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett

Auton. Agents Multi Agent Syst., 2022

Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent.

Ian Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

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

Ian Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, Andrea Tacchetti

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021

Quantifying environment and population diversity in multi-agent reinforcement learning.

Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett

CoRR, 2021

Collaborating with Humans without Human Data.

DJ Strouse, Kevin R. McKee, Matt M. Botvinick, Edward Hughes, Richard Everett

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Modelling Cooperation in Network Games with Spatio-Temporal Complexity.

Michiel A. Bakker, Richard Everett, Laura Weidinger, Iason Gabriel, William S. Isaac, Joel Z. Leibo, Edward Hughes

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences.

Raphael Köster, Kevin R. McKee, Richard Everett, Laura Weidinger, William S. Isaac, Edward Hughes, Edgar A. Duéñez-Guzmán, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo

CoRR, 2020

Negotiating team formation using deep reinforcement learning.

Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel

Artif. Intell., 2020

Bounds and dynamics for empirical game theoretic analysis.

Karl Tuyls, Julien Pérolat, Marc Lanctot, Edward Hughes, Richard Everett, Joel Z. Leibo, Csaba Szepesvári, Thore Graepel

Auton. Agents Multi Agent Syst., 2020

Learning to Play No-Press Diplomacy with Best Response Policy Iteration.

Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents.

Richard Everett, Adam D. Cobb, Andrew Markham, Stephen J. Roberts

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus.

Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Learning Against Non-Stationary Agents with Opponent Modelling and Deep Reinforcement Learning.

Richard Everett, Stephen J. Roberts

Proceedings of the 2018 AAAI Spring Symposia, 2018

2016

The anatomy of online deception: what makes automated text convincing?

Richard M. Everett, Jason R. C. Nurse, Arnau Erola

Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016