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ORCIDRichard Everett 0001
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- affiliation: DeepMind Technologies Limite, London, United Kingdom
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2020 – today
- 2023
[i14]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 abs/2305.00768 (2023)- 2022
- [j4]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): 21 (2022) - [j3]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:
Developing, evaluating and scaling learning agents in multi-agent environments. AI Commun. 35(4): 271-284 (2022) - [c9]Ian 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: 498-506 - [c8]Ian 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: 507-515 - [i13]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 abs/2201.01816 (2022) - [i12]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:
Learning Robust Real-Time Cultural Transmission without Human Data. CoRR abs/2203.00715 (2022) - [i11]Elise van der Pol, Ian Gemp, Yoram Bachrach, Richard Everett:
Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering. CoRR abs/2207.14589 (2022) - [i10]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:
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments. CoRR abs/2209.10958 (2022) - 2021
- [c7]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: 1455-1457 - [c6]DJ Strouse, Kevin R. McKee, Matt M. Botvinick, Edward Hughes, Richard Everett:
Collaborating with Humans without Human Data. NeurIPS 2021: 14502-14515 - [i9]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 abs/2102.06911 (2021) - [i8]Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett:
Quantifying environment and population diversity in multi-agent reinforcement learning. CoRR abs/2102.08370 (2021) - [i7]Ian 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 abs/2106.01285 (2021) - [i6]DJ Strouse, Kevin R. McKee, Matt M. Botvinick, Edward Hughes, Richard Everett:
Collaborating with Humans without Human Data. CoRR abs/2110.08176 (2021) - 2020
- [j2]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): 7 (2020) - [j1]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: 103356 (2020) - [c5]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:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS 2020 - [i5]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:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR abs/2006.04635 (2020) - [i4]Ian 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 abs/2010.00575 (2020) - [i3]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:
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. CoRR abs/2010.09054 (2020) - [i2]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 abs/2010.10380 (2020)
2010 – 2019
- 2019
- [c4]Richard Everett, Adam D. Cobb, Andrew Markham, Stephen J. Roberts:
Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents. AAMAS 2019: 1943-1945 - 2018
- [c3]Richard Everett, Stephen J. Roberts:
Learning Against Non-Stationary Agents with Opponent Modelling and Deep Reinforcement Learning. AAAI Spring Symposia 2018 - [c2]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: 1254-1262 - [i1]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 abs/1802.10446 (2018) - 2016
- [c1]Richard M. Everett, Jason R. C. Nurse, Arnau Erola:
The anatomy of online deception: what makes automated text convincing? SAC 2016: 1115-1120
Coauthor Index
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