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Thore Graepel — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Thore Graepel
16
papers ·
1780
citations ·
51
h-index
Google DeepMind (United Kingdom)
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
2017 · 1084 citations
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
2017 · 278 citations
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
2017 · 142 citations
A Multi-agent Reinforcement Learning Model Of Common-pool Resource Appropriation
2017 · 70 citations
Emergent Coordination Through Competition
2019 · 56 citations
Negotiating Team Formation Using Deep Reinforcement Learning
2020 · 37 citations
Biases for Emergent Communication in Multi-agent Reinforcement Learning
2019 · 29 citations
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
2021 · 23 citations
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
2020 · 18 citations
Learning Shared Representations in Multi-task Reinforcement Learning
2016 · 13 citations
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences
2020 · 11 citations
A multi-agent reinforcement learning model of reputation and cooperation in human groups
2021 · 6 citations
Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game
2025
Game Theoretic Rating in N-player general-sum games with Equilibria
2022
Top co-authors
Joel Z. Leibo
· 7
Marc Lanctot
· 6
Edward Hughes
· 4
Raphael Koster
· 4
Angeliki Lazaridou
· 3
Charles Beattie
· 3
Karl Tuyls
· 3
Richard Everett
· 3
Vinicius Zambaldi
· 3
David Silver
· 2
Guy Lever
· 2
Ian Gemp
· 2
Topics
Multi-Agent
Game AI
Value-Based
Model-Based RL
Meta-RL
Exploration
Policy Gradient
Safe RL