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Marc Lanctot — most-cited papers & profile · Reinforcement Learning
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Marc Lanctot
22
papers ·
2271
citations ·
33
h-index
Google (Canada)
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Semantic Scholar ↗
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Most-cited papers
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
2017 · 1084 citations
Deep Q-learning from Demonstrations
2017 · 307 citations
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
2017 · 278 citations
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
2022 · 150 citations
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
2017 · 142 citations
OpenSpiel: A Framework for Reinforcement Learning in Games
2019 · 107 citations
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
2018 · 71 citations
Emergent Communication through Negotiation
2018 · 47 citations
Negotiating Team Formation Using Deep Reinforcement Learning
2020 · 37 citations
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
2020 · 18 citations
Neural Replicator Dynamics
2019 · 13 citations
The Advantage Regret-Matching Actor-Critic
2020 · 8 citations
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
2022 · 6 citations
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning
2023 · 3 citations
Solving Common-Payoff Games with Approximate Policy Iteration
2021
Top co-authors
Karl Tuyls
· 7
Thore Graepel
· 6
Daniel Hennes
· 5
Julien Perolat
· 5
Vinicius Zambaldi
· 5
Audrunas Gruslys
· 4
Finbarr Timbers
· 4
Jean-Baptiste Lespiau
· 4
Shayegan Omidshafiei
· 4
Angeliki Lazaridou
· 3
David Silver
· 3
Dustin Morrill
· 3
Topics
Game AI
Multi-Agent
Value-Based
Model-Based RL
Policy Gradient
Exploration
Safe RL
Offline RL
Meta-RL