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J. Andrew Bagnell — most-cited papers & profile · Reinforcement Learning
← authors
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overview
J. Andrew Bagnell
16
papers ·
182
citations ·
56
h-index
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Most-cited papers
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
2017 · 91 citations
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning
2018 · 38 citations
Dual Policy Iteration
2018 · 25 citations
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
2019 · 16 citations
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
2022 · 7 citations
Hybrid Inverse Reinforcement Learning
2024 · 2 citations
REBEL: Reinforcement Learning via Regressing Relative Rewards
2024 · 2 citations
Inverse Reinforcement Learning without Reinforcement Learning
2023 · 1 citations
To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning
2025
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning
2025
Exploration in Action Space
2020
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
2023
The Virtues of Pessimism in Inverse Reinforcement Learning
2024
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage
2024
Hybrid Reinforcement Learning from Offline Observation Alone
2024
Top co-authors
Gokul Swamy
· 6
Wen Sun
· 6
Sanjiban Choudhury
· 5
Yuda Song
· 5
Zhiwei Steven Wu
· 4
Anirudh Vemula
· 3
Byron Boots
· 3
Aarti Singh
· 2
Aarti Singh
· 2
Geoffrey J. Gordon
· 2
Wen Sun
· 2
Akshay Krishnamurthy
· 1
Topics
Model-Based RL
Offline RL
Policy Gradient
Exploration
Value-Based
RLHF & Alignment
Game AI
Meta-RL