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2020 – today
- 2022
[j6]Daniel Graves
, Johannes Günther, Jun Luo:
Affordance as general value function: a computational model. Adapt. Behav. 30(4): 307-327 (2022)- [c13]Hongyao Tang, Zhaopeng Meng, Jianye Hao, Chen Chen, Daniel Graves, Dong Li, Changmin Yu, Hangyu Mao, Wulong Liu, Yaodong Yang, Wenyuan Tao, Li Wang:
What about Inputting Policy in Value Function: Policy Representation and Policy-Extended Value Function Approximator. AAAI 2022: 8441-8449 - [c12]Jun Jin, Daniel Graves, Cameron Haigh, Jun Luo, Martin Jägersand:
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning. ICRA 2022: 3616-3623 - 2021
- [c11]Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor:
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. AAMAS 2021: 51-56 - [c10]Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh, Jun Jin, Jun Luo:
Learning robust driving policies without online exploration. ICRA 2021: 13186-13193 - [i13]Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou-Ammar, Jun Wang, Matthew E. Taylor:
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. CoRR abs/2102.07659 (2021) - [i12]Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh, Jun Jin, Jun Luo:
Learning robust driving policies without online exploration. CoRR abs/2103.08070 (2021) - 2020
- [c9]Kristopher De Asis, Alan Chan, Silviu Pitis, Richard S. Sutton, Daniel Graves:
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. AAAI 2020: 3741-3748 - [c8]Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Zhengbang Zhu, Yihan Ni, Nhat M. Nguyen, Mohamed Elsayed, Haitham Ammar, Alexander I. Cowen-Rivers, Sanjeevan Ahilan, Zheng Tian, Daniel Palenicek, Kasra Rezaee, Peyman Yadmellat, Kun Shao, Dong Chen, Baokuan Zhang, Hongbo Zhang, Jianye Hao, Wulong Liu, Jun Wang:
SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving. CoRL 2020: 264-285 - [c7]Jun Jin, Nhat M. Nguyen, Nazmus Sakib, Daniel Graves, Hengshuai Yao, Martin Jägersand:
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans. ICRA 2020: 6979-6985 - [i11]Daniel Graves, Kasra Rezaee, Sean Scheideman:
Perception as prediction using general value functions in autonomous driving applications. CoRR abs/2001.09113 (2020) - [i10]Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh, Jun Jin:
Learning predictive representations in autonomous driving to improve deep reinforcement learning. CoRR abs/2006.15110 (2020) - [i9]Hongyao Tang, Zhaopeng Meng, Jianye Hao, Chen Chen, Daniel Graves, Dong Li, Wulong Liu, Yaodong Yang:
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator. CoRR abs/2010.09536 (2020) - [i8]Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat M. Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander I. Cowen-Rivers, Zheng Tian, Daniel Palenicek, Haitham Bou-Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang:
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving. CoRR abs/2010.09776 (2020) - [i7]Daniel Graves, Johannes Günther, Jun Luo:
Affordance as general value function: A computational model. CoRR abs/2010.14289 (2020) - [i6]Jun Jin, Daniel Graves, Cameron Haigh, Jun Luo, Martin Jägersand:
Offline Learning of Counterfactual Perception as Prediction for Real-World Robotic Reinforcement Learning. CoRR abs/2011.05857 (2020) - [i5]Daniel Graves, Jun Jin, Jun Luo:
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning. CoRR abs/2012.14942 (2020)
2010 – 2019
- 2019
- [j5]Kuldeep R. Kurte, Jibonananda Sanyal, Anne Berres, Dalton D. Lunga, Mark Coletti, Hsiuhan Lexie Yang, Daniel Graves, Benjamin Liebersohn, Amy N. Rose:
Performance analysis and optimization for scalable deployment of deep learning models for country-scale settlement mapping on Titan supercomputer. Concurr. Comput. Pract. Exp. 31(20) (2019) - [c6]Yingxu Wang, Omar A. Zatarain, Tony Tsai, Daniel Graves:
Sequence Learning for Images Recognition in Videos with Differential Neural Networks. ICCI*CC 2019: 117-122 - [c5]Daniel Graves, Kasra Rezaee, Sean Scheideman:
Perception as prediction using general value functions in autonomous driving applications. IROS 2019: 1202-1209 - [c4]Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White:
Importance Resampling for Off-policy Prediction. NeurIPS 2019: 1797-1807 - [i4]Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White:
Importance Resampling for Off-policy Prediction. CoRR abs/1906.04328 (2019) - [i3]Kristopher De Asis, Alan Chan, Silviu Pitis, Richard S. Sutton, Daniel Graves:
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. CoRR abs/1909.03906 (2019) - [i2]Jun Jin, Nhat M. Nguyen, Nazmus Sakib, Daniel Graves, Hengshuai Yao, Martin Jägersand:
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans. CoRR abs/1911.03074 (2019) - [i1]Borislav Mavrin, Daniel Graves, Alan Chan:
Efficient decorrelation of features using Gramian in Reinforcement Learning. CoRR abs/1911.08610 (2019) - 2018
- [c3]Yingxu Wang, Henry Leung, Marina L. Gavrilova, Omar A. Zatarain, Daniel Graves, Jianhua Lu, Newton Howard, Sam Kwong, Phillip C.-Y. Sheu, Shushma Patel:
A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks. ICCI*CC 2018: 6-15 - 2014
- [j4]Xin Xu, Zhenhua Huang, Daniel Graves, Witold Pedrycz:
A Clustering-Based Graph Laplacian Framework for Value Function Approximation in Reinforcement Learning. IEEE Trans. Cybern. 44(12): 2613-2625 (2014) - 2012
- [j3]Daniel Graves, Joost Noppen, Witold Pedrycz:
Clustering with proximity knowledge and relational knowledge. Pattern Recognit. 45(7): 2633-2644 (2012) - 2010
- [j2]Daniel Graves, Witold Pedrycz:
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study. Fuzzy Sets Syst. 161(4): 522-543 (2010) - [c2]Daniel Graves, Witold Pedrycz:
Proximity fuzzy clustering and its application to time series clustering and prediction. ISDA 2010: 49-54
2000 – 2009
- 2009
- [j1]Daniel Graves, Witold Pedrycz:
Fuzzy prediction architecture using recurrent neural networks. Neurocomputing 72(7-9): 1668-1678 (2009) - [c1]Daniel Graves, Witold Pedrycz:
Multivariate Segmentation of Time Series with Differential Evolution. IFSA/EUSFLAT Conf. 2009: 1108-1113 - 2007
- [p1]Daniel Graves, Witold Pedrycz:
Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative Study. Analysis and Design of Intelligent Systems using Soft Computing Techniques 2007: 140-149
Coauthor Index
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