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
Johannes Ackermann — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Johannes Ackermann
7
papers ·
90
citations ·
13
h-index
University Hospital Schleswig-Holstein · Universitäts Hautklinik Kiel · RIKEN Center for Advanced Intelligence Project · University of Lübeck
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
2019 · 70 citations
Mitigating Reward Hacking in RLHF via Advantage Sign Robustness
2026
Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards
2026
Offline Reinforcement Learning with Domain-Unlabeled Data
2024
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity
2024
Top co-authors
Masashi Sugiyama
· 3
Soichiro Nishimori
· 2
Takayuki Osa
· 2
Masashi Sugiyama
· 1
Masashi Sugiyama
· 1
Michael Noukhovitch
· 1
Shinnosuke Ono
· 1
Takashi Ishida
· 1
Takashi Ishida
· 1
Volker Gabler
· 1
Xin-Qiang Cai
· 1
Topics
RLHF & Alignment
Policy Gradient
Safe RL
Offline RL
Multi-Agent
Value-Based
Model-Based RL