Martin Riedmiller
About
In The Last Decade
Martin Riedmiller
86 papers receiving 22.1k citations
Hit Papers
Peers
Comparison fields: 5 of 211
- Artificial Intelligence 10.2k
- Electrical and Electronic Engineering 4.4k
- Computer Vision and Pattern Recognition 4.3k
- Control and Systems Engineering 4.3k
- Computer Networks and Communications 3.7k
Countries citing papers authored by Martin Riedmiller
This map shows the geographic impact of Martin Riedmiller's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Martin Riedmiller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Riedmiller more than expected).
Fields of papers citing papers by Martin Riedmiller
This network shows the impact of papers produced by Martin Riedmiller. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Martin Riedmiller. The network helps show where Martin Riedmiller may publish in the future.
Co-authorship network of co-authors of Martin Riedmiller
This figure shows the co-authorship network connecting the top 25 collaborators of Martin Riedmiller. A scholar is included among the top collaborators of Martin Riedmiller based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Martin Riedmiller. Martin Riedmiller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | A Constrained Multi-Objective Reinforcement Learning Framework | 2 |
| 4 | Data-efficient Hindsight Off-policy Option Learning | 3 |
| 5 | Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion | 2 |
| 6 | Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning | 17 |
| 7 | V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control | 3 |
| 8 | Controlling biological neural networks with deep reinforcement learning. | 1 |
| 9 | Learning an Embedding Space for Transferable Robot Skills | 66 |
| 10 | Maximum a Posteriori Policy Optimisation | 10 |
| 11 | Learning by Playing - Solving Sparse Reward Tasks from Scratch | 48 |
| 12 | Human-level control through deep reinforcement learning breakdown → | 17153 |
| 13 | Discriminative Unsupervised Feature Learning with Convolutional Neural Networks breakdown → | 289 |
| 14 | Electricity demand forecasting using Gaussian processes | 25 |
| 15 | Deep learning of visual control policies. | 13 |
| 16 | 4 | |
| 17 | Reinforcement Learning in a Nutshell | 12 |
| 18 | Reducing policy degradation in neuro-dynamic programming. | 8 |
| 19 | Learning a Partial Behavior for a Competitive Robotic Soccer Agent | 8 |
| 20 | A neural reinforcement learning approach to learn local dispatching policies in production scheduling | 42 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.