Daniel Graves
Daniel Graves
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Cited by
Cited by
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study
D Graves, W Pedrycz
Fuzzy sets and systems 161 (4), 522-543, 2010
Smarts: An open-source scalable multi-agent rl training school for autonomous driving
M Zhou, J Luo, J Villella, Y Yang, D Rusu, J Miao, W Zhang, M Alban, ...
Conference on Robot Learning, 264-285, 2021
Fuzzy prediction architecture using recurrent neural networks
D Graves, W Pedrycz
Neurocomputing 72 (7-9), 1668-1678, 2009
Mapless navigation among dynamics with social-safety-awareness: a reinforcement learning approach from 2d laser scans
J Jin, NM Nguyen, N Sakib, D Graves, H Yao, M Jagersand
2020 IEEE international conference on robotics and automation (ICRA), 6979-6985, 2020
Fuzzy c-means, gustafson-kessel fcm, and kernel-based fcm: A comparative study
D Graves, W Pedrycz
Analysis and design of intelligent systems using soft computing techniques …, 2007
Importance resampling for off-policy prediction
M Schlegel, W Chung, D Graves, J Qian, M White
Advances in Neural Information Processing Systems 32, 2019
Diverse auto-curriculum is critical for successful real-world multiagent learning systems
Y Yang, J Luo, Y Wen, O Slumbers, D Graves, HB Ammar, J Wang, ...
arXiv preprint arXiv:2102.07659, 2021
A clustering-based graph Laplacian framework for value function approximation in reinforcement learning
X Xu, Z Huang, D Graves, W Pedrycz
IEEE Transactions on Cybernetics 44 (12), 2613-2625, 2014
Fixed-horizon temporal difference methods for stable reinforcement learning
K De Asis, A Chan, S Pitis, R Sutton, D Graves
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3741-3748, 2020
Performance of kernel-based fuzzy clustering
D Graves, W Pedrycz
Electronics Letters 43 (25), 1, 2007
A survey and formal analyses on sequence learning methodologies and deep neural networks
Y Wang, H Leung, M Gavrilova, O Zatarain, D Graves, J Lu, N Howard, ...
2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive …, 2018
Multivariate Segmentation of Time Series with Differential Evolution.
D Graves, W Pedrycz
IFSA/EUSFLAT Conf., 1108-1113, 2009
What about inputting policy in value function: Policy representation and policy-extended value function approximator
H Tang, Z Meng, J Hao, C Chen, D Graves, D Li, C Yu, H Mao, W Liu, ...
Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8441-8449, 2022
Clustering with proximity knowledge and relational knowledge
D Graves, J Noppen, W Pedrycz
Pattern recognition 45 (7), 2633-2644, 2012
Sequence Learning for Images Recognition in Videos with Differential Neural Networks
Y Wang, O Zatarain, T Tsai, D Graves
2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive …, 2019
Proximity fuzzy clustering and its application to time series clustering and prediction
D Graves, W Pedrycz
2010 10th International conference on intelligent systems design and …, 2010
Offline learning of counterfactual perception as prediction for real-world robotic reinforcement learning
J Jin, D Graves, C Haigh, J Luo, M Jagersand
arXiv preprint arXiv:2011.05857, 2020
Perception as prediction using general value functions in autonomous driving applications
D Graves, K Rezaee, S Scheideman
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
Method and system for controlling safety of ego and social objects
DM Graves
US Patent 11,364,936, 2022
Learning predictive representations in autonomous driving to improve deep reinforcement learning
D Graves, NM Nguyen, K Hassanzadeh, J Jin
arXiv preprint arXiv:2006.15110, 2020
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