| Learning to control self-assembling morphologies: a study of generalization via modularity D Pathak*, C Lu*, T Darrell, P Isola, AA Efros Advances in Neural Information Processing Systems 32, *equal contribution, 2019 | 138 | 2019 |
| Discovered policy optimisation C Lu*, J Kuba*, A Letcher, L Metz, C Schroeder de Witt, J Foerster Advances in Neural Information Processing Systems, *equal contribution …, 2022 | 76 | 2022 |
| The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery C Lu*, C Lu*, RT Lange*, J Foerster, J Clune, D Ha arXiv preprint arXiv:2408.06292, 2024 | 67* | 2024 |
| Structured state space models for in-context reinforcement learning C Lu, Y Schroecker, A Gu, E Parisotto, J Foerster, S Singh, F Behbahani Advances in Neural Information Processing Systems, 2023 | 67 | 2023 |
| Model-free opponent shaping C Lu, T Willi, CAS De Witt, J Foerster International Conference on Machine Learning, 14398-14411, 2022 | 55 | 2022 |
| Discovering Evolution Strategies via Meta-Black-Box Optimization RT Lange, T Schaul, Y Chen, T Zahavy, V Dallibard, C Lu, S Singh, ... 11th International Conference on Learning Representations, ICLR 2023, 2023 | 46 | 2023 |
| JaxMARL: Multi-Agent RL Environments and Algorithms in JAX A Rutherford*, B Ellis*, M Gallici*, J Cook, A Lupu, G Ingvarsson, T Willi, ... Proceedings of the 23rd International Conference on Autonomous Agents and …, 2024 | 33* | 2024 |
| Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization RT Lange, T Schaul, Y Chen, C Lu, T Zahavy, V Dalibard, S Flennerhag Genetic and Evolutionary Computation Conference Companion (GECCO), 2023 | 31 | 2023 |
| Proximal learning with opponent-learning awareness S Zhao, C Lu, RB Grosse, J Foerster Advances in Neural Information Processing Systems 35, 26324-26336, 2022 | 24 | 2022 |
| Adversarial Cheap Talk C Lu, T Willi, A Letcher, J Foerster International Conference on Machine Learning, 2023 | 22 | 2023 |
| Discovering Temporally-Aware Reinforcement Learning Algorithms MT Jackson*, C Lu*, L Kirsch, RT Lange, S Whiteson, JN Foerster arXiv preprint arXiv:2402.05828, *Equal contribution, 2024 | 17 | 2024 |
| Centralized model and exploration policy for multi-agent RL Q Zhang, C Lu, A Garg, J Foerster International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2022 | 17 | 2022 |
| JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading S Frey*, K Li*, P Nagy*, S Sapora, C Lu, S Zohren, J Foerster, A Calinescu arXiv preprint arXiv:2308.13289, 2023 | 11 | 2023 |
| Discovering general reinforcement learning algorithms with adversarial environment design MT Jackson, M Jiang, J Parker-Holder, R Vuorio, C Lu, G Farquhar, ... Advances in Neural Information Processing Systems 36, 79980-79998, 2023 | 9 | 2023 |
| Scaling Opponent Shaping to High Dimensional Games A Khan*, T Willi*, N Kwan*, A Tacchetti, C Lu, E Grefenstette, ... arXiv preprint arXiv:2312.12568, 2023 | 8* | 2023 |
| Discovering Preference Optimization Algorithms with and for Large Language Models C Lu*, S Holt*, C Fanconi*, AJ Chan, J Foerster, M van der Schaar, ... arXiv preprint arXiv:2406.08414, 2024 | 6 | 2024 |
| Pax: Scalable opponent shaping in jax T Willi, A Khan, N Kwan, M Samvelyan, C Lu, J Foerster | 6* | 2023 |
| Relu to the rescue: Improve your on-policy actor-critic with positive advantages A Jesson, C Lu, G Gupta, N Beltran-Velez, A Filos, JN Foerster, Y Gal arXiv preprint arXiv:2306.01460, 2023 | 5 | 2023 |
| Arbitrary Order Meta-Learning with Simple Population-Based Evolution C Lu, S Towers, J Foerster ALIFE 2023: The 2023 Conference on Artificial Life, 2023 | 5 | 2023 |
| Analysing the Sample Complexity of Opponent Shaping K Fung*, Q Zhang*, C Lu, J Wan, T Willi, J Foerster arXiv preprint arXiv:2402.05782, 2024 | 4* | 2024 |
Jakob FoersterAssociate Professor, University of OxfordVerified email at eng.ox.ac.uk
