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Noah Golowich — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Noah Golowich
12
papers ·
34
citations ·
13
h-index
Microsoft (United States) · Moscow Institute of Thermal Technology · Microsoft Research (United Kingdom) · Microsoft Research New York City (United States)
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Most-cited papers
Independent Policy Gradient Methods for Competitive Reinforcement Learning
2021 · 22 citations
Planning in Observable POMDPs in Quasipolynomial Time
2022 · 4 citations
Learning in Observable POMDPs, without Computationally Intractable Oracles
2022 · 3 citations
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
2022 · 2 citations
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
2023 · 2 citations
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
2023 · 1 citations
Can Q-Learning be Improved with Advice?
2021
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
2023
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles
2023
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning
2024
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions
2024
The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation
2024
Top co-authors
Ankur Moitra
· 7
Dylan J. Foster
· 5
Dhruv Rohatgi
· 3
Alexander Rakhlin
· 2
and Dhruv Rohatgi
· 1
Ayush Sekhari
· 1
Constantinos Daskalakis
· 1
Dean P. Foster
· 1
Jian Qian
· 1
Sham M. Kakade
· 1
Yanjun Han
· 1
Topics
Value-Based
Exploration
Model-Based RL
Multi-Agent
Game AI
Meta-RL
Policy Gradient
Offline RL