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Alekh Agarwal — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Alekh Agarwal
36
papers ·
761
citations ·
41
h-index
Google (United States)
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
2016 · 153 citations
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
2019 · 111 citations
Making Contextual Decisions with Low Technical Debt
2016 · 80 citations
Off-Policy Policy Gradient with State Distribution Correction
2019 · 46 citations
Safe Reinforcement Learning via Curriculum Induction
2020 · 41 citations
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
2020 · 36 citations
Provably Good Batch Reinforcement Learning Without Great Exploration
2020 · 36 citations
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
2019 · 33 citations
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
2020 · 28 citations
Hierarchical Imitation and Reinforcement Learning
2018 · 27 citations
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
2016 · 24 citations
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
2016 · 20 citations
On Oracle-Efficient PAC RL with Rich Observations
2018 · 20 citations
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
2018 · 17 citations
Bellman-consistent Pessimism for Offline Reinforcement Learning
2021 · 16 citations
Top co-authors
Akshay Krishnamurthy
· 9
John Langford
· 6
Nan Jiang
· 6
Christoph Dann
· 4
Adith Swaminathan
· 3
Aditya Modi
· 3
Ching-An Cheng
· 3
Robert E. Schapire
· 3
Sham Kakade
· 3
Emma Brunskill
· 2
Jinglin Chen
· 2
Lin F. Yang
· 2
Topics
Exploration
Model-Based RL
Value-Based
Policy Gradient
Offline RL
Safe RL
RLHF & Alignment
Meta-RL
Game AI
Multi-Agent