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Gerald Tesauro — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Gerald Tesauro
13
papers ·
379
citations ·
46
h-index
Google Scholar ↗
Semantic Scholar ↗
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Most-cited papers
On-line Policy Improvement using Monte-Carlo Search
2025 · 213 citations
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
2017 · 87 citations
Hybrid Reinforcement Learning with Expert State Sequences
2019 · 18 citations
Learning Abstract Options
2018 · 17 citations
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
2020 · 12 citations
The Eigenoption-Critic Framework
2017 · 7 citations
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
2020 · 7 citations
Deep RL With Information Constrained Policies: Generalization in Continuous Control
2020 · 5 citations
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
2022 · 5 citations
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games
2020
Context-Specific Representation Abstraction for Deep Option Learning
2021
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria
2022
Top co-authors
Matthew Riemer
· 7
Dong-Ki Kim
· 4
Jonathan P. How
· 4
Miao Liu
· 4
Miao Liu
· 3
Murray Campbell
· 3
Tim Klinger
· 3
Chris R. Sims
· 2
Chuangchuang Sun
· 2
Jakob N. Foerster
· 2
Marwa Abdulhai
· 2
Mo Yu
· 2
Topics
Game AI
Policy Gradient
Meta-RL
Model-Based RL
Multi-Agent
Exploration
Value-Based
RLHF & Alignment
Offline RL