| Path planning and obstacle avoidance for AUV: A review C Cheng, Q Sha, B He, G Li Ocean Engineering 235, 109355, 2021 | 167 | 2021 |
| Human-centered reinforcement learning: A survey G Li, R Gomez, K Nakamura, B He IEEE Transactions on Human-Machine Systems 49 (4), 337-349, 2019 | 128 | 2019 |
| Deep interactive reinforcement learning for path following of autonomous underwater vehicle Q Zhang, J Lin, Q Sha, B He, G Li IEEE Access 8, 24258-24268, 2020 | 88 | 2020 |
| A review on interactive reinforcement learning from human social feedback J Lin, Z Ma, R Gomez, K Nakamura, B He, G Li IEEE Access 8, 120757-120765, 2020 | 86 | 2020 |
| Transferring policy of deep reinforcement learning from simulation to reality for robotics H Ju, R Juan, R Gomez, K Nakamura, G Li Nature Machine Intelligence, 1077-1087, 2022 | 44 | 2022 |
| Fish recognition using convolutional neural network G Ding, Y Song, J Guo, C Feng, G Li, B He, T Yan OCEANS 2017-Anchorage, 1-4, 2017 | 43 | 2017 |
| Using informative behavior to increase engagement in the TAMER framework G Li, H Hung, S Whiteson, WB Knox Proceedings of the 12th international conference on Autonomous agents and …, 2013 | 42 | 2013 |
| Side scan sonar segmentation using deep convolutional neural network Y Song, Y Zhu, G Li, C Feng, B He, T Yan OCEANS 2017-Anchorage, 1-4, 2017 | 37 | 2017 |
| Challenge balancing for personalised game spaces S Bakkes, S Whiteson, G Li, GV Vişniuc, E Charitos, N Heijne, ... 2014 IEEE Games Media Entertainment, 1-8, 2014 | 32 | 2014 |
| NavNet: AUV navigation through deep sequential learning X Zhang, B He, G Li, X Mu, Y Zhou, T Mang IEEE Access 8, 59845-59861, 2020 | 29 | 2020 |
| Segmentation of sidescan sonar imagery using markov random fields and extreme learning machine Y Song, B He, Y Zhao, G Li, Q Sha, Y Shen, T Yan, R Nian, A Lendasse IEEE Journal of Oceanic Engineering 44 (2), 502-513, 2018 | 28 | 2018 |
| Facial feedback for reinforcement learning: a case study and offline analysis using the TAMER framework G Li, H Dibeklioğlu, S Whiteson, H Hung Autonomous Agents and Multi-Agent Systems 34, 1-29, 2020 | 27 | 2020 |
| Multiple receptive field network (MRF-Net) for autonomous underwater vehicle fishing net detection using forward-looking sonar images R Qin, X Zhao, W Zhu, Q Yang, B He, G Li, T Yan Sensors 21 (6), 1933, 2021 | 24 | 2021 |
| PCA and kernel-based extreme learning machine for side-scan sonar image classification M Zhu, Y Song, J Guo, C Feng, G Li, T Yan, B He 2017 IEEE Underwater Technology (UT), 1-4, 2017 | 24 | 2017 |
| Using informative behavior to increase engagement while learning from human reward G Li, S Whiteson, WB Knox, H Hung Autonomous agents and multi-agent systems 30 (5), 826-848, 2016 | 24 | 2016 |
| Autonomous underwater vehicle formation control and obstacle avoidance using multi-agent generative adversarial imitation learning Z Fang, D Jiang, J Huang, C Cheng, Q Sha, B He, G Li Ocean Engineering 262, 112182, 2022 | 22 | 2022 |
| Sliding mode heading control for AUV based on continuous hybrid model-free and model-based reinforcement learning D Wang, Y Shen, J Wan, Q Sha, G Li, G Chen, B He Applied Ocean Research 118, 102960, 2022 | 22 | 2022 |
| Social interaction for efficient agent learning from human reward G Li, S Whiteson, WB Knox, H Hung Autonomous Agents and Multi-Agent Systems 32, 1-25, 2018 | 20 | 2018 |
| Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback G Li, R Gomez, K Nakamura, B He IEEE RO-MAN, 2018 | 17 | 2018 |
| Sensor fault diagnosis of autonomous underwater vehicle based on extreme learning machine X Li, Y Song, J Guo, C Feng, G Li, T Yan, B He 2017 IEEE Underwater Technology (UT), 1-5, 2017 | 17 | 2017 |
Randy GomezHonda Research Institute Japan在 honda-ri.jp 的电子邮件经过验证
