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Tom Schaul — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Tom Schaul
18
papers ·
2318
citations ·
41
h-index
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Semantic Scholar ↗
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Most-cited papers
StarCraft II: A New Challenge for Reinforcement Learning
2017 · 685 citations
Rainbow: Combining Improvements in Deep Reinforcement Learning
2017 · 428 citations
Deep Q-learning from Demonstrations
2017 · 307 citations
Reinforcement Learning with Unsupervised Auxiliary Tasks
2016 · 272 citations
FeUdal Networks for Hierarchical Reinforcement Learning
2017 · 252 citations
Successor Features for Transfer in Reinforcement Learning
2016 · 184 citations
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
2019 · 57 citations
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
2019 · 39 citations
Universal Successor Features Approximators
2018 · 24 citations
Policy Evaluation Networks
2020 · 14 citations
Adapting Behaviour for Learning Progress
2019 · 8 citations
When should agents explore?
2021 · 6 citations
Vision-Language Models as a Source of Rewards
2023 · 3 citations
Scaling Goal-based Exploration via Pruning Proto-goals
2023 · 1 citations
Conditional Importance Sampling for Off-Policy Learning
2019
Top co-authors
David Silver
· 7
Diana Borsa
· 6
John Quan
· 6
Hado van Hasselt
· 5
Andr\'e Barreto
· 4
Georg Ostrovski
· 4
R\'emi Munos
· 4
Will Dabney
· 4
Alexander Sasha Vezhnevets
· 2
Bilal Piot
· 2
Dan Horgan
· 2
Daniel Mankowitz
· 2
Topics
Value-Based
Meta-RL
Game AI
Exploration
Policy Gradient
Offline RL
Multi-Agent
Model-Based RL
RLHF & Alignment