Reinforcement Learning in Robotics
Reinforcement Learning in Robotics is a research topic within Artificial Intelligence. Science Explorer counts 21k research works in it since 1956. 21.0% of them reached the world's top 10% most cited for their field and year.
This cluster of papers encompasses a wide range of advancements in reinforcement learning algorithms and their applications, including deep learning, neural networks, robotics, autonomous control, policy gradient methods, multi-agent systems, model-based learning, curiosity-driven exploration, and simulation to real-world transfer.
- Reinforcement Learning
- Deep Learning
- Neural Networks
- Robotics
- Autonomous Control
- Policy Gradient
- Multi-Agent Systems
- Model-Based Learning
- Curiosity-Driven Exploration
- Simulation to Real-world Transfer
- Research works
- 21k fractional, since 1956
- In the world top 10%
- 4.3k per year above
- Top-10% rate
- 21.0% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +125% the tick is no change
Which countries lead Reinforcement Learning in Robotics research?
By volume, China and the United States publish the most (2.3k and 1.3k works in 2022–2025).
By volume, 2022–2025
- 1 China 2.3k works
- 2 United States 1.3k works
- 3 United Kingdom 316 works
- 4 Germany 303 works
- 5 Japan 266 works
- 6 India 262 works
- 7 Canada 190 works
- 8 South Korea 179 works
- 9 Italy 165 works
- 10 France 147 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Reinforcement Learning in Robotics research?
By volume in 2022–2025, Tsinghua University publishes the most Reinforcement Learning in Robotics research, followed by National University of Defense Technology and Shanghai Jiao Tong University.
By volume, 2022–2025
- 1 Tsinghua University China 95 works
- 2 National University of Defense Technology China 77 works
- 3 Shanghai Jiao Tong University China 61 works
- 4 Northwestern Polytechnical University China 53 works
- 5 Beihang University China 52 works
- 6 Beijing Institute of Technology China 50 works
- 7 Chinese Academy of Sciences China 48 works
- 8 Carnegie Mellon University United States 48 works
- 9 Harbin Institute of Technology China 48 works
- 10 Georgia Institute of Technology United States 38 works
Who are the leading researchers in Reinforcement Learning in Robotics?
The most-cited researchers publishing on Reinforcement Learning in Robotics include Scott Reed, Li Fei-Fei and Yoshua Bengio.
- 1 Scott Reed 17k citations
- 2 Li Fei-Fei 17k citations
- 3 Yoshua Bengio 17k citations
- 4 Jitendra Malik 12k citations
- 5 Ion Stoica 9.6k citations
- 6 Oriol Vinyals 7.4k citations
- 7 Soumith Chintala 6.9k citations
- 8 Trevor Darrell 6.7k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Reinforcement Learning in Robotics research done?
The largest centres of Reinforcement Learning in Robotics research in 2022–2025 are Beijing (China), Shanghai (China), Nanjing (China) and Xi'an (China).
Where is the best place to study Reinforcement Learning in Robotics?
Among universities, judged by research, Tsinghua University, National University of Defense Technology and Nanyang Technological University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Tsinghua University China | 66.7 | 23.1% | 7.2× | 95 | +165.7% |
| 2 | National University of Defense Technology China | 65.9 | 18.3% | 14.7× | 77 | +147.8% |
| 3 | Nanyang Technological University Singapore | 64.3 | 34.5% | 6.9× | 37 | +35.6% |
| 4 | University of California, Berkeley United States | 63.9 | 34.5% | 5.4× | 31 | +271.5% |
| 5 | Beihang University China | 63.3 | 24.0% | 7.0× | 52 | +264.2% |
| 6 | Carnegie Mellon University United States | 62.5 | 18.2% | 17.5× | 48 | +109.0% |
| 7 | Northeastern University China | 60.5 | 30.0% | 5.7× | 34 | +391.2% |
| 8 | University of Technology Sydney Australia | 58.5 | 44.6% | 4.6× | 14 | +69.7% |
| 9 | Beijing Institute of Technology China | 55.3 | 16.6% | 6.2× | 50 | +252.2% |
| 10 | Shanghai Jiao Tong University China | 54.4 | 19.7% | 4.2× | 61 | +239.8% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Reinforcement Learning in Robotics research growing?
Output in 2018–2022 was 125% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Reinforcement Learning in Robotics.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.