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 | 118 | 2019 |
Model-free opponent shaping C Lu, T Willi, CAS De Witt, J Foerster International Conference on Machine Learning, 14398-14411, 2022 | 38 | 2022 |
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 | 36 | 2022 |
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 | 32 | 2023 |
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 | 30 | 2023 |
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 | 17 | 2023 |
Adversarial Cheap Talk C Lu, T Willi, A Letcher, J Foerster International Conference on Machine Learning, 2023 | 17 | 2023 |
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 | 15 | 2022 |
Proximal learning with opponent-learning awareness S Zhao, C Lu, RB Grosse, J Foerster Advances in Neural Information Processing Systems 35, 26324-26336, 2022 | 14 | 2022 |
JaxMARL: Multi-Agent RL Environments in JAX A Rutherford*, B Ellis*, M Gallici*, J Cook, A Lupu, G Ingvarsson, T Willi, ... arXiv preprint arXiv:2311.10090, *Equal Contribution, 2023 | 6 | 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, 2024 | 4 | 2024 |
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 | 4 | 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 | 2* | 2024 |
Pax: Multi-agent learning in jax T Willi, A Khan, N Kwan, M Samvelyan, C Lu, J Foerster | 2 | 2023 |
Context and History Aware Other-Shaping A Khan, N Kwan, T Willi, C Lu, A Tacchetti, JN Foerster | 2* | 2022 |
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 | 1 | 2024 |
Leading the Pack: N-player Opponent Shaping A Souly, T Willi, A Khan, R Kirk, C Lu, E Grefenstette, T Rocktäschel arXiv preprint arXiv:2312.12564, 2023 | 1 | 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 | 1 | 2023 |
Revisiting Recurrent Reinforcement Learning with Memory Monoids S Morad, C Lu, R Kortvelesy, S Liwicki, J Foerster, A Prorok arXiv preprint arXiv:2402.09900, 2024 | | 2024 |
Meta-learning the mirror map in policy mirror descent C Alfano, S Towers, S Sapora, C Lu, P Rebeschini arXiv preprint arXiv:2402.05187, 2024 | | 2024 |