14.2.6.2 Reinforcement Learning

Chapter Contents (Back)
Reinforcement Learning. Positive and negative reinforcement.
See also Transfer Learning from Other Tasks, Other Classes.
See also Continunal Learning, Incremental Learning.

Shoeleh, F.[Farzaneh], Asadpour, M.[Masoud],
Graph based skill acquisition and transfer Learning for continuous reinforcement learning domains,
PRL(87), No. 1, 2017, pp. 104-116.
Elsevier DOI 1703
Reinforcement learning BibRef

Koo, S.[Sangjun], Yu, H.[Hwanjo], Lee, G.G.[Gary Geunbae],
Adversarial approach to domain adaptation for reinforcement learning on dialog systems,
PRL(128), 2019, pp. 467-473.
Elsevier DOI 1912
Dialog systems, Reinforcement learning, Domain adaptation, Transfer learning, Deep Q Network, Adversarial networks BibRef

Agarwal, M.[Mridul], Aggarwal, V.[Vaneet],
Blind decision making: Reinforcement learning with delayed observations,
PRL(150), 2021, pp. 176-182.
Elsevier DOI 2109
BibRef

Hwang, R.[Rakhoon], Lee, H.J.[Han-Jin], Hwang, H.J.[Hyung Ju],
Option compatible reward inverse reinforcement learning,
PRL(154), 2022, pp. 83-89.
Elsevier DOI 2202
Reinforcement learning, Inverse reinforcement learning, Transfer learning, Machine learning BibRef

Nicholaus, I.T.[Isack Thomas], Kang, D.K.[Dae-Ki],
Robust experience replay sampling for multi-agent reinforcement learning,
PRL(155), 2022, pp. 135-142.
Elsevier DOI 2203
Reinforcement learning, Multi-agent, Sampling, Experience replay BibRef

Wang, J.[Jiao], Zhang, L.[Lemin], He, Z.Q.[Zhi-Qiang], Zhu, C.[Can], Zhao, Z.[Zihui],
Erlang planning network: An iterative model-based reinforcement learning with multi-perspective,
PR(128), 2022, pp. 108668.
Elsevier DOI 2205
Model-based reinforcement learning, Multi-perspective, Bi-level, Planning, Trajectory imagination BibRef

Li, M.[Min], Huang, T.Y.[Tian-Yi], Zhu, W.[William],
Clustering experience replay for the effective exploitation in reinforcement learning,
PR(131), 2022, pp. 108875.
Elsevier DOI 2208
Reinforcement learning, Clustering, Experience replay, Exploitation efficiency, Time division BibRef

Tosatto, S.[Samuele], Carvalho, J.[Joăo], Peters, J.[Jan],
Batch Reinforcement Learning With a Nonparametric Off-Policy Policy Gradient,
PAMI(44), No. 10, October 2022, pp. 5996-6010.
IEEE DOI 2209
Mathematical model, Estimation, Kernel, Reinforcement learning, Monte Carlo methods, Task analysis, Closed-form solutions, nonparametric estimation BibRef

Guo, S.Q.[Shang-Qi], Yan, Q.[Qi], Su, X.[Xin], Hu, X.L.[Xiao-Lin], Chen, F.[Feng],
State-Temporal Compression in Reinforcement Learning With the Reward-Restricted Geodesic Metric,
PAMI(44), No. 9, September 2022, pp. 5572-5589.
IEEE DOI 2208
Measurement, Task analysis, Reinforcement learning, Neural networks, Time-domain analysis, reinforcement learning (RL) BibRef

Xu, T.[Tian], Li, Z.N.[Zi-Niu], Yu, Y.[Yang],
Error Bounds of Imitating Policies and Environments for Reinforcement Learning,
PAMI(44), No. 10, October 2022, pp. 6968-6980.
IEEE DOI 2209
Planning, Reinforcement learning, Cloning, Complexity theory, Supervised learning, Decision making, Upper bound, model-based reinforcement learning BibRef

Li, Y.[Yun], Liu, Z.[Zhe], Yao, L.[Lina], Wang, X.Z.[Xian-Zhi], McAuley, J.[Julian], Chang, X.J.[Xiao-Jun],
An Entropy-Guided Reinforced Partial Convolutional Network for Zero-Shot Learning,
CirSysVideo(32), No. 8, August 2022, pp. 5175-5186.
IEEE DOI 2208
Convolution, Feature extraction, Semantics, Visualization, Training, Optimization, Kernel, Zero-shot learning, reinforcement learning, image representation BibRef

Feng, J.[Jie], Li, D.[Di], Gu, J.[Jing], Cao, X.H.[Xiang-Hai], Shang, R.H.[Rong-Hua], Zhang, X.R.[Xiang-Rong], Jiao, L.C.[Li-Cheng],
Deep Reinforcement Learning for Semisupervised Hyperspectral Band Selection,
GeoRS(60), 2022, pp. 1-19.
IEEE DOI 2112
Hyperspectral imaging, Reinforcement learning, Optimization, Deep learning, Task analysis, Neural networks, semisupervised learning BibRef

Akrour, R.[Riad], Tateo, D.[Davide], Peters, J.[Jan],
Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts,
PAMI(44), No. 10, October 2022, pp. 6795-6806.
IEEE DOI 2209
Task analysis, Complexity theory, Approximation algorithms, Neural networks, Trajectory, Reinforcement learning, robotics BibRef

Zhang, M.Y.[Meng-Yang], Tian, G.H.[Guo-Hui], Gao, H.B.[Huan-Bing], Zhang, Y.[Ying],
Autonomous Generation of Service Strategy for Household Tasks: A Progressive Learning Method With A Priori Knowledge and Reinforcement Learning,
CirSysVideo(32), No. 11, November 2022, pp. 7473-7488.
IEEE DOI 2211
Correlation, Task analysis, Reinforcement learning, Artificial neural networks. BibRef

Li, W.H.[Wen-Hao], Wang, X.F.[Xiang-Feng], Jin, B.[Bo], Luo, D.[Dijun], Zha, H.Y.[Hong-Yuan],
Structured Cooperative Reinforcement Learning With Time-Varying Composite Action Space,
PAMI(44), No. 11, November 2022, pp. 8618-8634.
IEEE DOI 2210
Agriculture, Aerospace electronics, Task analysis, Reinforcement learning, Games, Carbon dioxide, Robustness, time-varying action space BibRef

Zhu, R.[Rongbo], Li, M.Y.[Meng-Yao], Liu, H.[Hao], Liu, L.[Lu], Ma, M.[Maode],
Federated Deep Reinforcement Learning-Based Spectrum Access Algorithm With Warranty Contract in Intelligent Transportation Systems,
ITS(24), No. 1, January 2023, pp. 1178-1190.
IEEE DOI 2301
Contracts, Warranties, Resource management, Quality of service, Real-time systems, Heuristic algorithms, Vehicle dynamics, quality of service BibRef

Hu, T.M.[Tian-Meng], Luo, B.[Biao], Yang, C.H.[Chun-Hua], Huang, T.W.[Ting-Wen],
MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning,
PAMI(45), No. 10, October 2023, pp. 12098-12112.
IEEE DOI 2310
BibRef

Zhao, L.Y.[Lin-Ya], Tan, K.[Kun], Wang, X.[Xue], Ding, J.W.[Jian-Wei], Liu, Z.X.[Zhao-Xian], Ma, H.L.[Hui-Lin], Han, B.[Bo],
Hyperspectral Feature Selection for SOM Prediction Using Deep Reinforcement Learning and Multiple Subset Evaluation Strategies,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Huang, F.X.[Fu-Xian], Ji, N.[Naye], Ni, H.J.[Hua-Jian], Li, S.J.[Shi-Jian], Li, X.[Xi],
Adaptive cooperative exploration for reinforcement learning from imperfect demonstrations,
PRL(165), 2023, pp. 176-182.
Elsevier DOI 2301
Reinforcement learning, Imitation learning, Cooperative exploration, Imperfect demonstrations, BibRef

Gomez, D.[Diego], Quijano, N.[Nicanor], Giraldo, L.F.[Luis Felipe],
Information Optimization and Transferable State Abstractions in Deep Reinforcement Learning,
PAMI(45), No. 4, April 2023, pp. 4782-4793.
IEEE DOI 2303
Task analysis, Reinforcement learning, Multitasking, Transfer learning, Optimization, Standards, Behavioral sciences, information theory BibRef

Zhu, Z.D.[Zhuang-Di], Lin, K.X.[Kai-Xiang], Jain, A.K.[Anil K.], Zhou, J.[Jiayu],
Transfer Learning in Deep Reinforcement Learning: A Survey,
PAMI(45), No. 11, November 2023, pp. 13344-13362.
IEEE DOI 2310
Survey, Transfer Learning. BibRef

Saengkyongam, S.[Sorawit], Thams, N.[Nikolaj], Peters, J.[Jonas], Pfister, N.[Niklas],
Invariant Policy Learning: A Causal Perspective,
PAMI(45), No. 7, July 2023, pp. 8606-8620.
IEEE DOI 2306
Training, Visualization, Reinforcement learning, Random variables, Particle measurements, Heuristic algorithms, off-policy learning BibRef

Huang, H.C.[Han-Chi], Ye, D.H.[De-Heng], Shen, L.[Li], Liu, W.[Wei],
Curriculum-Based Asymmetric Multi-Task Reinforcement Learning,
PAMI(45), No. 6, June 2023, pp. 7258-7269.
IEEE DOI 2305
Task analysis, Training, Multitasking, Reinforcement learning, Optimization, Interference, Supervised learning, asymmetric multi-task learning BibRef

Zhang, T.R.[Tian-Ren], Guo, S.Q.[Shang-Qi], Tan, T.[Tian], Hu, X.L.[Xiao-Lin], Chen, F.[Feng],
Adjacency Constraint for Efficient Hierarchical Reinforcement Learning,
PAMI(45), No. 4, April 2023, pp. 4152-4166.
IEEE DOI 2303
Task analysis, Reinforcement learning, Training, Random variables, Postal services, Markov processes, Games, adjacency constraint BibRef

Deng, Z.H.[Zhi-Hong], Fu, Z.[Zuyue], Wang, L.X.[Ling-Xiao], Yang, Z.[Zhuoran], Bai, C.J.[Chen-Jia], Zhou, T.Y.[Tian-Yi], Wang, Z.R.[Zhao-Ran], Jiang, J.[Jing],
False Correlation Reduction for Offline Reinforcement Learning,
PAMI(46), No. 2, February 2024, pp. 1199-1211.
IEEE DOI 2401
False correlation, offline reinforcement learning, uncertainty estimation BibRef


Choi, H.[Hyesong], Lee, H.[Hunsang], Song, W.[Wonil], Jeon, S.[Sangryul], Sohn, K.H.[Kwang-Hoon], Min, D.B.[Dong-Bo],
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning,
CVPR23(15072-15082)
IEEE DOI 2309
BibRef

Huang, Y.R.[Yang-Ru], Peng, P.X.[Pei-Xi], Zhao, Y.F.[Yi-Fan], Zhai, Y.P.[Yun-Peng], Xu, H.R.[Hao-Ran], Tian, Y.H.[Yong-Hong],
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning,
ICCV23(176-185)
IEEE DOI 2401
BibRef

Zhai, Y.P.[Yun-Peng], Peng, P.X.[Pei-Xi], Zhao, Y.F.[Yi-Fan], Huang, Y.R.[Yang-Ru], Tian, Y.H.[Yong-Hong],
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation,
ICCV23(207-216)
IEEE DOI 2401
BibRef

Choi, H.[Hyesong], Lee, H.[Hunsang], Jeong, S.W.[Seong-Won], Min, D.B.[Dong-Bo],
Environment Agnostic Representation for Visual Reinforcement learning,
ICCV23(263-273)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liu, H.Z.[Hao-Zhe], Zhuge, M.[Mingchen], Li, B.[Bing], Wang, Y.H.[Yu-Hui], Faccio, F.[Francesco], Ghanem, B.[Bernard], Schmidhuber, J.[Jürgen],
Learning to Identify Critical States for Reinforcement Learning from Videos,
ICCV23(1955-1965)
IEEE DOI Code:
WWW Link. 2401
BibRef

Nie, C.[Chang], Wang, G.M.[Guang-Ming], Liu, Z.[Zhe], Cavalli, L.[Luca], Pollefeys, M.[Marc], Wang, H.S.[He-Sheng],
RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation,
ICCV23(9857-9866)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liu, S.[Siao], Chen, Z.Y.[Zhao-Yu], Liu, Y.[Yang], Wang, Y.Z.[Yu-Zheng], Yang, D.[Dingkang], Zhao, Z.[Zhile], Zhou, Z.Q.[Zi-Qing], Yi, X.[Xie], Li, W.[Wei], Zhang, W.Q.[Wen-Qiang], Gan, Z.X.[Zhong-Xue],
Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentation,
ICCV23(23379-23389)
IEEE DOI 2401
BibRef

Klinghoffer, T.[Tzofi], Tiwary, K.[Kushagra], Behari, N.[Nikhil], Agrawalla, B.[Bhavya], Raskar, R.[Ramesh],
DISeR: Designing Imaging Systems with Reinforcement Learning,
ICCV23(23575-23585)
IEEE DOI Code:
WWW Link. 2401
BibRef

Fang, F.[Fen], Liang, W.Y.[Wen-Yu], Wu, Y.[Yan], Xu, Q.L.[Qian-Li], Lim, J.H.[Joo-Hwee],
Improving Generalization of Reinforcement Learning Using a Bilinear Policy Network,
ICIP22(991-995)
IEEE DOI 2211
Representation learning, Visualization, Reinforcement learning, Object detection, Games, Feature extraction, Path planning, Generalization BibRef

Lucchesi, N.[Nicoló], Carta, A.[Antonio], Lomonaco, V.[Vincenzo], Bacciu, D.[Davide],
Avalanche RL: A Continual Reinforcement Learning Library,
CIAP22(I:524-535).
Springer DOI 2205
BibRef

Wang, X.D.[Xu-Dong], Lian, L.[Long], Yu, S.X.[Stella X.],
Unsupervised Visual Attention and Invariance for Reinforcement Learning,
CVPR21(6673-6683)
IEEE DOI 2111
Training, Visualization, Annotations, Reinforcement learning, Manuals, Benchmark testing BibRef

García-Ramírez, J.[Jesús], Morales, E.[Eduardo], Escalante, H.J.[Hugo Jair],
Multi-source Transfer Learning for Deep Reinforcement Learning,
MCPR21(131-140).
Springer DOI 2108
BibRef

Zhang, Z.Z.[Zi-Zhao], Pfister, T.[Tomas],
Learning Fast Sample Re-weighting Without Reward Data,
ICCV21(705-714)
IEEE DOI 2203
Training, Costs, Limiting, Computational modeling, Reinforcement learning, Noise robustness, Noise measurement, Machine learning architectures and formulations BibRef

Hong, J.[Jie], Fang, P.F.[Peng-Fei], Li, W.H.[Wei-Hao], Zhang, T.[Tong], Simon, C.[Christian], Harandi, M.[Mehrtash], Petersson, L.[Lars],
Reinforced Attention for Few-Shot Learning and Beyond,
CVPR21(913-923)
IEEE DOI 2111
Image recognition, Computational modeling, Reinforcement learning, Prediction algorithms, Data models, Pattern recognition BibRef

Zhang, Y.S.[You-Shan], Ye, H.[Hui], Davison, B.D.[Brian D.],
Adversarial Reinforcement Learning for Unsupervised Domain Adaptation,
WACV21(635-644)
IEEE DOI 2106
BibRef
Earlier: A1, A3, Only:
Adversarial Continuous Learning in Unsupervised Domain Adaptation,
DLPR20(672-687).
Springer DOI 2103
Adaptation models, Computational modeling, Neural networks, Reinforcement learning, Feature extraction. BibRef

Lomonaco, V., Desai, K., Culurciello, E., Maltoni, D.,
Continual Reinforcement Learning in 3D Non-stationary Environments,
CLVision20(999-1008)
IEEE DOI 2008
Task analysis, Learning (artificial intelligence), Benchmark testing, Color, Training, Complexity theory BibRef

Zhu, L.C.[Lin-Chao], Arik, S.Ö.[Sercan Ö.], Yang, Y.[Yi], Pfister, T.[Tomas],
Learning to Transfer Learn: Reinforcement Learning-based Selection for Adaptive Transfer Learning,
ECCV20(XXVII:342-358).
Springer DOI 2011
BibRef

Manteghi, S.[Sajad], Parvin, H.[Hamid], Heidarzadegan, A.[Ali], Nemati, Y.[Yasser],
Multitask Reinforcement Learning in Nondeterministic Environments: Maze Problem Case,
MCPR15(64-73).
Springer DOI 1506
BibRef

Garcia, E.O.[Esteban O.], de Cote, E.M.[Enrique Munoz], Morales, E.F.[Eduardo F.],
Qualitative Transfer for Reinforcement Learning with Continuous State and Action Spaces,
CIARP13(I:198-205).
Springer DOI 1311
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Subspace Clustering, Subspace Learning .


Last update:Jan 30, 2024 at 20:33:16