| Large language models are neurosymbolic reasoners M Fang, S Deng, Y Zhang, Z Shi, L Chen, M Pechenizkiy, J Wang AAAI 2024, 2024 | 23 | 2024 |
| Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach Y Zhang, Y Du, B Huang, Z Wang, J Wang, M Fang, M Pechenizkiy Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 17 | 2023 |
| RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking F Zhong, X Bi, Y Zhang, W Zhang, Y Wang AAAI 2023 (Oral), 2023 | 16 | 2023 |
| COOM: a game benchmark for continual reinforcement learning T Tomilin, M Fang, Y Zhang, M Pechenizkiy Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment Z Wang, Y Du, Y Zhang, M Fang, B Huang NeurIPS CRL 2024, 2023 | 2 | 2023 |
| RuAG: Learned-rule-augmented Generation for Large Language Models Y Zhang, P Xiao, L Wang, C Zhang, M Fang, Y Du, Y Puzyrev, R Yao, ... ICLR2024, 2024 | 1 | 2024 |
| Large Action Models: From Inception to Implementation L Wang, F Yang, C Zhang, J Lu, J Qian, S He, P Zhao, B Qiao, R Huang, ... arXiv preprint arXiv:2412.10047, 2024 | | 2024 |
| A Causality-Inspired Spatial-Temporal Return Decomposition Approach for Multi-Agent Reinforcement Learning Y Zhang, Y Du, B Huang, M Fang, M Pechenizkiy NeurIPS 2024 Causal Representation Learning Workshop, 0 | | |
Meng FangUniversity of Liverpool確認したメール アドレス: liverpool.ac.uk