Jakob Foerster
Associate Professor
Works on deep multi-agent reinforcement learning, emergent communication and zero-shot coordination.
Jakob Foerster's lab at Oxford works on reinforcement learning, reasoning & neuro-symbolic AI and planning, control & multi-robot systems. 27 papers since 2023, 19 at top venues, mostly NeurIPS and ICML. 47 PhD students and 11 master's students. 7 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 70%
- PhD students
- 47
- MS students
- 11
- alumni listed
- 7
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 4
- 14 papers2024 · T1 10
- 8 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, AAMAS
Research focuspaper titles that use each topic's words
- Reinforcement learning13 papers
- Planning, control & multi-robot systems4 papers
- Reasoning & neuro-symbolic AI0 papers
- zero-shot0 papers
- coordination0 papers
Paper list
From the lab homepage.
20255 papers
- NeurIPS (T1)A Clean Slate for Offline Reinforcement Learning
- ICML (T1)ADIOS: Antibody Development via Opponent Shaping
- NeurIPS (T1)AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds via Self-Improvement
- RLCAn Optimisation Framework for Unsupervised Environment Design
- ICLR (T1)Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks
202414 papers
- AAMAS (T1)Analysing the Sample Complexity of Opponent Shaping
- NeurIPS (T1)Artificial Generational Intelligence: Cultural Accumulation in RL
- NeurIPS (T1)Can Learned Optimization Make RL Less Difficult?
- ICML (T1)Craftax: A Lightning-Fast Benchmark for Open-Ended RL
- ICLR (T1)Discovering Temporally-Aware Reinforcement Learning Algorithms
- ICML (T1)EvIL: Evolution Strategies for Generalisable Imitation Learning
- ICLR (T1)Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers
- NeurIPS Datasets and BenchmarksJaxMARL: Multi-Agent RL Environments and Algorithms in JAX
- RLCMixture of Experts in a Mixture of RL Settings
- ICML (T1)Mixture of Experts Unlock Parameter Scaling for Deep RL
- ICAIFMixtures of Experts for Scaling up Neural Networks in Order Execution
- NeurIPS (T1)No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
20238 papers
- ICML (T1)Adversarial Cheap Talk
- ICLR (T1)Adversarial Diversity for Hanabi
- NeurIPS (T1)Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
- ICAIFGenerative AI for End-to-End Limit Order Book Modelling
- ICAIFJAX-LOB: A GPU-Accelerated Limit Order Book Simulator to Unlock Large-Scale RL for Trading
- ICLR (T1)Perfectly Secure Steganography using Minimum Entropy Coupling
- NeurIPS Datasets and BenchmarksSMACv2: An Improved Benchmark for Cooperative Multi-Agent RL
- NeurIPS (T1)Structured State Space Models for In-Context RL
Where alumni went7 listed
Apply to Oxford2027-28
DPhil in Computer Science
- Round
- 2027-28 entry (January round)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- USCMU
Fei Fang
- Reasoning & neuro-symbolic AI
- USMIT
Jonathan Ragan-Kelley
- Rendering & appearance
- GBOxford
Luca Furieri
- Planning, control & multi-robot systems
- USMIT
Patrick Jaillet
- Reinforcement learning
- USUSC
Xiaohui Chen
- Learning theory & optimization
- GBUCL
Jun Wang
- Reinforcement learning
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
Oxfordcloses