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Maximilian Igl — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Maximilian Igl
11
papers ·
212
citations ·
13
h-index
Nvidia (United Kingdom) · University of Hong Kong
Google Scholar ↗
Semantic Scholar ↗
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Most-cited papers
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
2019 · 65 citations
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
2019 · 57 citations
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
2017 · 28 citations
Deep Variational Reinforcement Learning for POMDPs
2018 · 20 citations
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning
2020 · 11 citations
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
2020 · 10 citations
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
2020 · 3 citations
Communicating via Markov Decision Processes
2021
Top co-authors
Shimon Whiteson
· 7
Luisa Zintgraf
· 4
Katja Hofmann
· 3
Gregory Farquhar
· 2
Tim Rockt\"aschel
· 2
Wendelin Boehmer
· 2
Cheng Zhang
· 1
Christian Schroeder de Witt
· 1
Cong Lu
· 1
Frank Wood
· 1
Jakob Foerster
· 1
Jelena Luketina
· 1
Topics
Exploration
Meta-RL
Model-Based RL
Value-Based
Game AI
Policy Gradient
Safe RL
Multi-Agent