| Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks G Papoudakis, F Christianos, L Schäfer, SV Albrecht Conference on Neural Information Processing Systems, Track on Datasets and …, 2020 | 258* | 2020 |
| Shared experience actor-critic for multi-agent reinforcement learning F Christianos, L Schäfer, S Albrecht Advances in neural information processing systems 33, 10707-10717, 2020 | 164 | 2020 |
| Multi-agent reinforcement learning: Foundations and modern approaches SV Albrecht, F Christianos, L Schäfer Massachusetts Institute of Technology: Cambridge, MA, USA, 2024 | 64 | 2024 |
| Decoupled reinforcement learning to stabilise intrinsically-motivated exploration L Schäfer, F Christianos, JP Hanna, SV Albrecht Proceedings of the 21st International Conference on Autonomous Agents and …, 2022 | 36* | 2022 |
| Deep reinforcement learning for multi-agent interaction IH Ahmed, C Brewitt, I Carlucho, F Christianos, M Dunion, E Fosong, ... Ai Communications 35 (4), 357-368, 2022 | 14 | 2022 |
| Scalable multi-agent reinforcement learning for warehouse logistics with robotic and human co-workers A Krnjaic, RD Steleac, JD Thomas, G Papoudakis, L Schäfer, AWK To, ... arXiv preprint arXiv:2212.11498, 2022 | 12 | 2022 |
| Robust on-policy sampling for data-efficient policy evaluation in reinforcement learning R Zhong, D Zhang, L Schäfer, S Albrecht, J Hanna Advances in Neural Information Processing Systems 35, 37376-37388, 2022 | 11* | 2022 |
| Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning L Schäfer, O Slumbers, S McAleer, Y Du, SV Albrecht, D Mguni Adaptive and Learning Agents Workshop, AAMAS 2023, 2023 | 6 | 2023 |
| Curiosity in Multi-Agent Reinforcement Learning L Schäfer University of Edinburgh, 2019 | 6 | 2019 |
| Learning task embeddings for teamwork adaptation in multi-agent reinforcement learning L Schäfer, F Christianos, A Storkey, SV Albrecht arXiv preprint arXiv:2207.02249, 2022 | 5 | 2022 |
| Learning temporally-consistent representations for data-efficient reinforcement learning T McInroe, L Schäfer, SV Albrecht arXiv preprint arXiv:2110.04935, 2021 | 5 | 2021 |
| Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments A Andres, L Schäfer, E Villar-Rodriguez, SV Albrecht, J Del Ser Adaptive and Learning Agents Workshop, AAMAS 2023, 2023 | 2 | 2023 |
| Visual encoders for data-efficient imitation learning in modern video games L Schäfer, L Jones, A Kanervisto, Y Cao, T Rashid, R Georgescu, ... | 1 | 2023 |
| Task generalisation in multi-agent reinforcement learning L Schäfer Proceedings of the 21st International Conference on Autonomous Agents and …, 2022 | 1 | 2022 |
| Domain-Dependent Policy Learning using Neural Networks in Classical Planning L Schäfer Saarland University, 2018 | 1 | 2018 |
| Learning Representations for Reinforcement Learning with Hierarchical Forward Models T McInroe, L Schäfer, SV Albrecht Deep Reinforcement Learning Workshop NeurIPS 2022, 0 | 1* | |
| Autonomous Agents Research Group S Albrecht, F Christianos, L Schäfer, T McInroe, M Dunion, A Jelley, ... | | 2020 |
| The UK Multi-Agent Systems Symposium L Schäfer | | 2020 |
| Reinforcement Learning for Video Game Playing L Schäfer | | 2019 |
| Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning T McInroe, L Schäfer, SV Albrecht Transactions on Machine Learning Research, 0 | | |
Stefano V. AlbrechtSchool of Informatics, University of Edinburgh在 ed.ac.uk 的电子邮件经过验证
