Jakob Foerster
- Artificial Intelligence top 1%
- Computer Networks and Communications top 5%
- Control and Systems Engineering top 5%
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Shimon WhitesonNantas NardelliGregory FarquharTriantafyllos AfourasNando de FreitasAlon RubinLiora LasNachum Ulanovsky
- Topics
- Reinforcement Learning in Robotics (11 papers)Misinformation and Its Impacts (3 papers)Topic Modeling (3 papers)
- Partner nations
- United KingdomUnited StatesIsrael
In The Last Decade
Jakob Foerster
28 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 1.1k
- Computer Networks and Communications 409
- Control and Systems Engineering 247
- Electrical and Electronic Engineering 215
- Computer Vision and Pattern Recognition 214
Countries citing papers authored by Jakob Foerster
This map shows the geographic impact of Jakob Foerster'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 Jakob Foerster with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jakob Foerster more than expected).
Fields of papers citing papers by Jakob Foerster
This network shows the impact of papers produced by Jakob Foerster. 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 Jakob Foerster. The network helps show where Jakob Foerster may publish in the future.
Co-authorship network of co-authors of Jakob Foerster
This figure shows the co-authorship network connecting the top 25 collaborators of Jakob Foerster. A scholar is included among the top collaborators of Jakob Foerster 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 Jakob Foerster. Jakob Foerster is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 6 | |
| 6 | 7 | |
| 7 | 13 | |
| 8 | 10 | |
| 9 | Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning | 2 |
| 10 | 94 | |
| 11 | 5 | |
| 12 | Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning | 0 |
| 13 | Robust Domain Randomization for Reinforcement Learning | 8 |
| 14 | A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs | 1 |
| 15 | Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning | 12 |
| 16 | 6 | |
| 17 | The Mechanics of n-Player Differentiable Games | 13 |
| 18 | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability | 6 |
| 19 | Learning to Communicate with Deep Multi−Agent Reinforcement Learning | 183 |
| 20 | 53 |
About Jakob Foerster
Jakob Foerster is a scholar working on Artificial Intelligence, Developmental Biology and Statistical and Nonlinear Physics, having authored 35 papers that have together received 1.9k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (11 papers), Misinformation and Its Impacts (3 papers) and Topic Modeling (3 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Developmental Biology (53 citations) and Computer Networks and Communications (409 citations). Jakob Foerster has collaborated with scholars based in United Kingdom, United States and Israel. Frequent co-authors include Shimon Whiteson, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Nando de Freitas, Alon Rubin, Liora Las, Nachum Ulanovsky, William R. Clements and Arseny Finkelstein. Their work appears in journals such as Nature, PLoS ONE and Artificial Intelligence.
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.