| Monotonic value function factorisation for deep multi-agent reinforcement learning T Rashid, M Samvelyan, C Schroeder de Witt, G Farquhar, JN Foerster, ... Journal of Machine Learning Research 21, 2020 | 3811 | 2020 |
| The Starcraft Multi-Agent Challenge M Samvelyan, T Rashid, C Schroeder de Witt, G Farquhar, N Nardelli, ... AAMAS 2019, 2019 | 1566 | 2019 |
| Is independent learning all you need in the starcraft multi-agent challenge? CS De Witt, T Gupta, D Makoviichuk, V Makoviychuk, PHS Torr, M Sun, ... arXiv preprint arXiv:2011.09533, 2020 | 615 | 2020 |
| FACMAC: Factored Multi-Agent Centralised Policy Gradients B Peng, T Rashid, C Schroeder de Witt, PA Kamienny, P Torr, W Böhmer, ... Advances in Neural Information Processing Systems 34, 2021 | 401 | 2021 |
| Humanity's last exam L Phan, A Gatti, Z Han, N Li, J Hu, H Zhang, CBC Zhang, M Shaaban, ... arXiv preprint arXiv:2501.14249, 2025 | 301 | 2025 |
| Foundational challenges in assuring alignment and safety of large language models U Anwar, A Saparov, J Rando, D Paleka, M Turpin, P Hase, ES Lubana, ... arXiv preprint arXiv:2404.09932, 2024 | 296 | 2024 |
| Multi-Agent Common Knowledge Reinforcement Learning C Schroeder de Witt, J Foerster, G Farquhar, P Torr, W Boehmer, ... Advances in Neural Information Processing Systems, 9927-9939, 2019 | 151* | 2019 |
| Discovered policy optimisation C Lu, J Kuba, A Letcher, L Metz, C Schroeder de Witt, J Foerster Advances in Neural Information Processing Systems 35, 16455-16468, 2022 | 149 | 2022 |
| Randomized entity-wise factorization for multi-agent reinforcement learning S Iqbal, CAS De Witt, B Peng, W Böhmer, S Whiteson, F Sha International Conference on Machine Learning, 4596-4606, 2021 | 137* | 2021 |
| Jaxmarl: Multi-agent rl environments and algorithms in jax A Rutherford, B Ellis, M Gallici, J Cook, A Lupu, G Ingvarsson Juto, T Willi, ... Advances in Neural Information Processing Systems 37, 50925-50951, 2024 | 130 | 2024 |
| Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control C Schroeder de Witt, B Peng, PA Kamienny, P Torr, W Böhmer, ... arXiv preprint arXiv:2003.06709, 2020 | 118 | 2020 |
| Multi-agent risks from advanced ai L Hammond, A Chan, J Clifton, J Hoelscher-Obermaier, A Khan, ... arXiv preprint arXiv:2502.14143, 2025 | 97 | 2025 |
| Secret collusion among ai agents: Multi-agent deception via steganography S Motwani, M Baranchuk, M Strohmeier, V Bolina, P Torr, L Hammond, ... Advances in Neural Information Processing Systems 37, 73439-73486, 2024 | 81* | 2024 |
| Model-free opponent shaping C Lu, T Willi, CAS De Witt, J Foerster International Conference on Machine Learning, 14398-14411, 2022 | 79 | 2022 |
| Perfectly Secure Steganography Using Minimum Entropy Coupling C Schroeder de Witt*, S Sokota*, JZ Kolter, J Foerster, M Strohmeier ICLR 2023 (featured by Scientific American, Quanta Magazine, Bruce Schneier …, 2023 | 59* | 2023 |
| Risks and opportunities of open-source generative AI F Eiras, A Petrov, B Vidgen, C Schroeder, F Pizzati, K Elkins, ... arXiv preprint arXiv:2405.08597, 2024 | 54 | 2024 |
| The ZX-Calculus is Incomplete for Quantum Mechanics C Schroeder de Witt, V Zamdzhiev Quantum Physics and Logic (QPL) 2014, 2014 | 52* | 2014 |
| Malt: Improving reasoning with multi-agent llm training SR Motwani, C Smith, RJ Das, R Rafailov, I Laptev, PHS Torr, F Pizzati, ... arXiv preprint arXiv:2412.01928, 2024 | 45 | 2024 |
| Comparative global AI regulation: policy perspectives from the EU, China, and the US J Chun, CS de Witt, K Elkins arXiv preprint arXiv:2410.21279, 2024 | 38 | 2024 |
| Mirror learning: A unifying framework of policy optimisation J Grudzien, CAS De Witt, J Foerster International Conference on Machine Learning, 7825-7844, 2022 | 38* | 2022 |
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
