Richard Everett

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 26

Top Topics

Gaussian Process

Theoretic Analysis

Reinforcement Learning

Top Venues

Auton. Agents Multi Agent Syst.

Publications

Udari Madhushani, Kevin R. McKee, John P. Agapiou, Joel Z. Leibo, Richard Everett, Thomas W. Anthony, Edward Hughes, Karl Tuyls, Edgar A. Duéñez-Guzmán
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas. CoRR (2023)
Avishkar Bhoopchand, Bethanie Brownfield, Adrian Collister, Agustin Dal Lago, Ashley Edwards, Richard Everett, Alexandre Frechette, 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
Learning Robust Real-Time Cultural Transmission without Human Data. CoRR (2022)
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
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria. CoRR (2022)
Ian M. Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, Andrea Tacchetti
D3C: Reducing the Price of Anarchy in Multi-Agent Learning. AAMAS (2022)
Ian M. 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
Developing, evaluating and scaling learning agents in multi-agent environments. AI Commun. 35 (4) (2022)
Ian M. Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. AAMAS (2022)
Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning. Auton. Agents Multi Agent Syst. 36 (1) (2022)
Elise van der Pol, Ian M. Gemp, Yoram Bachrach, Richard Everett
Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering. CoRR (2022)
Ian M. Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar 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
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments. CoRR (2022)
Michiel A. Bakker, Richard Everett, Laura Weidinger, Iason Gabriel, William S. Isaac, Joel Z. Leibo, Edward Hughes
Modelling Cooperation in Network Games with Spatio-Temporal Complexity. AAMAS (2021)
Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett
Quantifying environment and population diversity in multi-agent reinforcement learning. CoRR (2021)
DJ Strouse, Kevin R. McKee, Matt M. Botvinick, Edward Hughes, Richard Everett
Collaborating with Humans without Human Data. CoRR (2021)
Ian M. Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. CoRR (2021)
DJ Strouse, Kevin R. McKee, Matt M. Botvinick, Edward Hughes, Richard Everett
Collaborating with Humans without Human Data. NeurIPS (2021)
Michiel A. Bakker, Richard Everett, Laura Weidinger, Iason Gabriel, William S. Isaac, Joel Z. Leibo, Edward Hughes
Modelling Cooperation in Network Games with Spatio-Temporal Complexity. CoRR (2021)
Ian M. Gemp, Kevin R. McKee, Richard Everett, Edgar A. Duéñez-Guzmán, Yoram Bachrach, David Balduzzi, Andrea Tacchetti
D3C: Reducing the Price of Anarchy in Multi-Agent Learning. CoRR (2020)
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS (2020)
Raphael Köster, Kevin R. McKee, Richard Everett, Laura Weidinger, William S. Isaac, Edward Hughes, Edgar A. Duéñez-Guzmán, Thore Graepel, Matthew Botvinick, Joel Z. Leibo
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. CoRR (2020)
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR (2020)
Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel
Negotiating Team Formation Using Deep Reinforcement Learning. CoRR (2020)
Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel
Negotiating team formation using deep reinforcement learning. Artif. Intell. 288 (2020)
Karl Tuyls, Julien Pérolat, Marc Lanctot, Edward Hughes, Richard Everett, Joel Z. Leibo, Csaba Szepesvári, Thore Graepel
Bounds and dynamics for empirical game theoretic analysis. Auton. Agents Multi Agent Syst. 34 (1) (2020)
Richard Everett, Adam D. Cobb, Andrew Markham, Stephen J. Roberts
Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents. AAMAS (2019)
Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus. CoRR (2018)
Richard Everett, Stephen J. Roberts
Learning Against Non-Stationary Agents with Opponent Modelling and Deep Reinforcement Learning. AAAI Spring Symposia (2018)
Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus. KDD (2018)