Awesome Reinforcement Learning
📄
Papers
🧭
Topics
🔥
Trending
🗺️
Map
🏆
Leaderboards
🎓
Learn
🤖
Ask AI
⋯
More
👥
Authors
📚
Reading Packs
📊
Datasets
🛠️
Tools
📰
News
📝
Blogs
✉️
Newsletter
🎯
Research Radar
🔖
Saved
+ Add Paper
☾
☀
← authors
·
overview
Loading author…
🤖
Ask AI
Mohammad Gheshlaghi Azar — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Mohammad Gheshlaghi Azar
18
papers ·
636
citations ·
24
h-index
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Noisy Networks for Exploration
2017 · 391 citations
Observe and Look Further: Achieving Consistent Performance on Atari
2018 · 87 citations
Minimax Regret Bounds for Reinforcement Learning
2017 · 51 citations
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning
2020 · 42 citations
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
2017 · 28 citations
A General Theoretical Paradigm to Understand Learning from Human Preferences
2023 · 14 citations
The Advantage Regret-Matching Actor-Critic
2020 · 8 citations
Geometric Entropic Exploration
2021 · 8 citations
An Analysis of Quantile Temporal-Difference Learning
2023 · 3 citations
Understanding Self-Predictive Learning for Reinforcement Learning
2022 · 1 citations
Nash Learning from Human Feedback
2023 · 1 citations
Offline Regularised Reinforcement Learning for Large Language Models Alignment
2024 · 1 citations
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion
2024
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
2022
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
2023
Top co-authors
Bilal Piot
· 9
R\'emi Munos
· 9
Michal Valko
· 6
Yunhao Tang
· 6
Olivier Pietquin
· 5
Daniele Calandriello
· 4
Matthieu Geist
· 4
Remi Munos
· 4
Bernardo Avila Pires
· 3
Daniel Guo
· 3
Will Dabney
· 3
Csaba Szepesv\'ari
· 2
Topics
Value-Based
Exploration
Policy Gradient
Game AI
Model-Based RL
RLHF & Alignment
Offline RL
Multi-Agent
Meta-RL
Safe RL