14.2.7.2 Reinforcement 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.H.[Zi-Hui],
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.[João], 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, 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

Huang, F.X.[Fu-Xian], Li, W.C.[Wei-Chao], Cui, J.B.[Jia-Bao], Fu, Y.J.[Yong-Jian], Li, X.[Xi],
Unified Curiosity-Driven Learning with Smoothed Intrinsic Reward Estimation,
PR(123), 2022, pp. 108352.
Elsevier DOI 2112
Reinforcement learning, Unified curiosity-driven exploration, Robust intrinsic reward, Task-relevant feature 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.Y.[Jia-Yu],
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.R.[Zhuo-Ran], 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

Zhang, L.Y.[Liang-Yu], Peng, Y.[Yang], Yang, W.H.[Wen-Hao], Zhang, Z.H.[Zhi-Hua],
Semi-Infinitely Constrained Markov Decision Processes and Provably Efficient Reinforcement Learning,
PAMI(46), No. 5, May 2024, pp. 3722-3735.
IEEE DOI 2404
Generalization of constrained Markov decision processes. Reinforcement learning, Complexity theory, Programming, Markov processes, Approximation algorithms, Marine vehicles, Costs, semi-infinitely programming BibRef

Rodrigues-da Silva, J.A.[Júnior Anderson], Grassi, V.[Valdir], Wolf, D.F.[Denis Fernando],
Maximum Entropy Inverse Reinforcement Learning Using Monte Carlo Tree Search for Autonomous Driving,
ITS(25), No. 9, September 2024, pp. 11552-11562.
IEEE DOI 2409
Trajectory, Behavioral sciences, Entropy, Autonomous vehicles, Vehicles, Cost function, Task analysis, Autonomous vehicles, merging, IRL BibRef

Pang, T.[Teng], Wu, G.Q.[Guo-Qiang], Zhang, Y.[Yan], Wang, B.Z.[Bing-Zheng], Yin, Y.L.[Yi-Long],
QFAE: Q-Function guided Action Exploration for offline deep reinforcement learning,
PR(158), 2025, pp. 111032.
Elsevier DOI 2411
Deep reinforcement learning, Offline reinforcement learning, Policy constraints, Action exploration, D4RL BibRef

Zheng, W.Q.[Wen-Qing], Sharan, S.P., Fan, Z.W.[Zhi-Wen], Wang, K.[Kevin], Xi, Y.H.[Yi-Han], Wang, Z.Y.[Zhang-Yang],
Symbolic Visual Reinforcement Learning: A Scalable Framework With Object-Level Abstraction and Differentiable Expression Search,
PAMI(47), No. 1, January 2025, pp. 400-412.
IEEE DOI 2412
Visualization, Optimization, Reinforcement learning, Representation learning, Neural networks, Vegetation, Planning, visual reinforcement learning (RL) BibRef

Liu, Y.Q.[Ya-Qiong], Zhao, T.Y.[Tong-Yu], Shou, G.[Guochu], Zhang, Y.[Yan],
Joint Optimization of Latency and Energy Consumption via Deep Reinforcement Learning for Proximity Detection in Road Networks,
ITS(25), No. 12, December 2024, pp. 19457-19468.
IEEE DOI 2412
Optimization, Servers, Energy consumption, Roads, Costs, Heuristic algorithms, Computational modeling, Internet of Vehicles (IoV) BibRef

Lee, S.H.[Sang-Hyun], Jung, Y.[Yoonjae], Seo, S.W.[Seung-Woo],
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments,
ITS(25), No. 12, December 2024, pp. 19522-19535.
IEEE DOI 2412
Autonomous Vehicles. Navigation, Attention mechanisms, Autonomous vehicles, Reinforcement learning, Heuristic algorithms, Standards, navigation BibRef

Liu, S.[Shunyu], Qing, Y.P.[Yun-Peng], Xu, S.Q.[Shu-Qi], Wu, H.Y.[Hong-Yan], Zhang, J.T.[Jiang-Tao], Cong, J.Y.[Jing-Yuan], Chen, T.H.[Tian-Hao], Liu, Y.F.[Yun-Fu], Song, M.L.[Ming-Li],
Curricular Subgoals for Inverse Reinforcement Learning,
ITS(26), No. 3, March 2025, pp. 3016-3027.
IEEE DOI Code:
WWW Link. 2503
Training, Trajectory, Reinforcement learning, Uncertainty, Optimization, Imitation learning, Blockchains, Benchmark testing, reward function BibRef

Wang, J.[Jie], Ye, M.X.[Ming-Xuan], Kuang, Y.F.[Yu-Fei], Yang, R.[Rui], Zhou, W.G.[Wen-Gang], Li, H.Q.[Hou-Qiang], Wu, F.[Feng],
Long-Term Feature Extraction via Frequency Prediction for Efficient Reinforcement Learning,
PAMI(47), No. 4, April 2025, pp. 3094-3110.
IEEE DOI 2503
Predictive models, Frequency-domain analysis, Feature extraction, Accuracy, Representation learning, Trajectory, Fourier transforms, fourier transform BibRef

Duan, J.L.[Jing-Liang], Wang, W.X.[Wen-Xuan], Xiao, L.M.[Li-Ming], Gao, J.X.[Jia-Xin], Li, S.B.E.[Sheng-Bo Eben], Liu, C.[Chang], Zhang, Y.Q.[Ya-Qin], Cheng, B.[Bo], Li, K.Q.[Ke-Qiang],
Distributional Soft Actor-Critic With Three Refinements,
PAMI(47), No. 5, May 2025, pp. 3935-3946.
IEEE DOI 2504
Standards, Estimation, Accuracy, Sensitivity, Stability analysis, Random variables, Q-learning, Tuning, Probability distribution, reinforcement learning BibRef

Liu, Z.C.[Zi-Chuan], Zhu, Y.Y.[Yuan-Yang], Wang, Z.[Zhi], Gao, Y.[Yang], Chen, C.L.[Chun-Lin],
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees,
PAMI(47), No. 5, May 2025, pp. 4090-4107.
IEEE DOI 2504
Decision trees, History, Q-learning, Training, Visualization, Decision making, Knowledge engineering, Data mining, Closed box, value decomposition BibRef

Liu, W.D.[Wei-Dong], Ma, J.H.[Jia-Hua], Mao, X.J.[Xiao-Jun], Tang, K.[Kejie],
A New Accelerated Off-Policy Stochastic Preconditioned TD(0) Algorithm,
PAMI(47), No. 9, September 2025, pp. 8088-8101.
IEEE DOI 2508
Convergence, Vectors, Function approximation, Monte Carlo methods, Approximation algorithms, Kernel, Estimation, Training, temporal-difference learning BibRef

Pi, B.[Bin], Deng, L.J.[Liang-Jian], Feng, M.[Minyu], Perc, M.[Matjaž], Kurths, J.[Jürgen],
Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure,
PAMI(47), No. 10, October 2025, pp. 8563-8582.
IEEE DOI 2510
Complex networks, Games, Analytical models, Exponential distribution, Evolution (biology), Q-learning, stochastic process BibRef

Yuan, C.Q.[Chang-Qing], Xie, Y.F.[Yong-Fang], Xie, S.[Shiwen], Tang, Z.H.[Zhao-Hui], Wu, Z.Z.[Zong-Ze],
Aggregated masked autoencoding for offline reinforcement learning,
PRL(197), 2025, pp. 312-318.
Elsevier DOI 2510
Offline reinforcement learning, Decision making, Self-supervised learning, Conditional sequence modeling BibRef

Du, W.[Wei], Chen, Z.[Zhengfan], Ding, S.F.[Shi-Fei], Zhang, C.L.[Cheng-Long], Guo, W.[Wei], Sun, Y.Q.[Yu-Qing], Yu, G.X.[Guo-Xian], Cui, L.Z.[Li-Zhen],
Learning multi-agent communication via graph contrastive learning,
PR(171), 2026, pp. 112093.
Elsevier DOI 2510
Multi-agent reinforcement learning, Graph neural networks, Communication learning BibRef

Ma, G.Z.[Guo-Zheng], Wang, Z.[Zhen], Yuan, Z.C.[Zhe-Cheng], Wang, X.Q.[Xue-Qian], Yuan, B.[Bo], Tao, D.C.[Da-Cheng],
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning,
IJCV(133), No. 10, October 2025, pp. 7368-7405.
Springer DOI 2511
BibRef

Zhang, F.[Fei], Yang, G.H.[Guang-Hong],
Safe Reinforcement Learning for Constrained Optimal Control With Provable Guarantees: Applications to Motion Planning,
ITS(26), No. 10, October 2025, pp. 14780-14791.
IEEE DOI 2511
Safety, Trajectory, Optimal control, Real-time systems, Planning, Convergence, Optimization, Autonomous vehicles, online learning BibRef

Li, X.[Xulong], Huangfu, W.[Wei], Xu, X.[Xinyi], Huo, J.H.[Jia-Hao], Long, K.P.[Ke-Ping],
Attention-Driven MARL for AoI Minimization in UAV-Assisted Intelligent Transport Systems,
ITS(26), No. 11, November 2025, pp. 20348-20362.
IEEE DOI 2511
Age of Information. Need current data. Multi Agent Reinforcement Learning. Autonomous aerial vehicles, Data collection, Attention mechanisms, Minimization, Sensors, Real-time systems BibRef

Liu, Z.Y.[Zi-Yuan], Zhuang, Y.[Yan], Wu, P.[Peng], Liu, Y.C.[Yuan-Chang],
IRIS: An information path planning method based on reinforcement learning and information-directed sampling,
PR(172), 2026, pp. 112400.
Elsevier DOI Code:
WWW Link. 2512
Informative path planning, Information-directed sampling, Reinforcement learning BibRef

Zhu, G.F.[Guo-Feng], Zhu, F.[Fei],
Action adversarial robust reinforcement learning via Stackelberg game,
PR(172), 2026, pp. 112580.
Elsevier DOI 2512
Robust reinforcement learning, Stackelberg game, Bi-level optimization, Action space attack BibRef

Taschin, F.[Federico], Lazraq, A.[Abderrahmane], Ozgunes, I.[Inci], Tonguz, O.K.[Ozan K.],
The Distribution Shift Problem in Transportation Networks Using Reinforcement Learning and AI,
ITS(26), No. 12, December 2025, pp. 22704-22715.
IEEE DOI 2512
Training, Neural networks, Adaptation models, Deep reinforcement learning, Data models, Heuristic algorithms, distributional shift BibRef

Chen, X.[Xing], Chen, H.C.[He-Chang], Chang, Y.[Yi],
Compact exploration for continuous action reinforcement learning,
PR(172), 2026, pp. 112739.
Elsevier DOI 2601
Optimistic critic, Compact exploration, Continuous action space, Deep reinforcement learning BibRef

Jiang, K.[Ke], Jiang, W.[Wen], Yao, L.[Li], Tan, X.Y.[Xiao-Yang],
Beyond non-expert demonstrations: Outcome-driven action constraint for offline reinforcement learning,
PR(172), 2026, pp. 112663.
Elsevier DOI 2601
Offline reinforcement learning, Non-expert data, Outcome-driven constraint, Trajectory stitching BibRef

Fan, Y.X.[Yi-Xin], Peng, H.X.[Hai-Xia], Su, Z.[Zhou], Li, Z.D.[Zhen-Dong], Luan, T.H.[Tom H.],
Proactive Collaborative Perception for CAVs: A Multi-Agent Reinforcement Learning Method,
ITS(27), No. 1, January 2026, pp. 270-285.
IEEE DOI 2601
Accuracy, Feature extraction, Sensors, Point cloud compression, Real-time systems, Optimization, Mathematical models, Laser radar, resource allocation BibRef

Chen, L.[Lin], Mo, Y.[Yang], Wang, Y.N.[Yao-Nan], Miao, Z.Q.[Zhi-Qiang], Zeng, K.[Kai], Feng, M.T.[Ming-Tao], Zhou, Z.[Zhen], Wang, S.[Sifei], Wang, D.[Danwei],
Self-Expert Imitation With Purifying Latent Feature for Generalization in Visual Reinforcement Learning,
ITS(27), No. 1, January 2026, pp. 960-971.
IEEE DOI 2601
Training, Reinforcement learning, Feature extraction, Visualization, Data augmentation, Neural networks, Data models, MDP BibRef

Chi, H.T.[Hao-Tian], Liu, Z.[Zhaogeng], Chen, X.[Xing], Qu, B.[Bohao], Hu, J.F.[Ji-Feng], Jiang, Y.[Yuan], Chen, H.[Hechang], Chang, Y.[Yi],
State transition difference prediction for deep reinforcement learning,
PR(173), 2026, pp. 112824.
Elsevier DOI 2601
Reinforcement learning, State representation learning, Forward prediction, Temporal difference operation BibRef

Kan, N.[Nuowen], Li, C.L.[Cheng-Lin], Jiang, Y.K.[Yuan-Kun], Dai, W.R.[Wen-Rui], Zou, J.[Junni], Xiong, H.K.[Hong-Kai], Toni, L.[Laura],
MERINA+: Improving Generalization for Neural Video Adaptation via Information-Theoretic Meta-Reinforcement Learning,
CirSysVideo(36), No. 1, January 2026, pp. 1185-1202.
IEEE DOI 2602
Throughput, Heuristic algorithms, Videos, Quality of experience, Bit rate, Training, Adaptation models, Inference algorithms, variational information bottleneck BibRef

Bao, Q.Y.[Qian-Yue], Liu, F.[Fang], Jiao, L.C.[Li-Cheng], Liu, Y.[Yang], Li, S.[Shuo], Li, L.L.[Ling-Ling], Liu, X.[Xu], Chen, P.[Puhua], Ma, W.P.[Wen-Ping],
ERFC: Energy-Aware Reinforcement Feedback Calibration for Zero-Shot Captioning,
CirSysVideo(36), No. 2, February 2026, pp. 2232-2246.
IEEE DOI 2602
Videos, Training, Adaptation models, Visualization, Calibration, Data models, Training data, Reinforcement learning, Optimization, reinforcement feedback BibRef

Shi, Y.C.[Yu-Chen], Duan, S.H.[Shi-Hong], Xu, C.[Cheng], Wang, R.[Ran], Ye, F.[Fangwen], Yuen, C.[Chau],
Dynamic Deep Factor Graph for Multi-Agent Reinforcement Learning,
PAMI(48), No. 3, March 2026, pp. 3417-3431.
IEEE DOI 2602
Heuristic algorithms, Collaboration, Vehicle dynamics, Optimization, Matrix decomposition, Approximation algorithms, dynamic collaboration BibRef

Yu, J.Y.[Ji-Yao], Zhu, B.[Bin], Chen, Y.[Yi], Xie, B.[Bo], Feng, X.L.[Xuan-Ling], Yan, H.F.[Hong-Fei], Zeng, J.[Jian], Wang, R.H.[Run-Hua],
Intelligent Acquisition of Dynamic Targets via Multi-Source Information: A Fusion Framework Integrating Deep Reinforcement Learning with Evidence Theory,
RS(18), No. 5, 2026, pp. 689.
DOI Link 2603
BibRef

Sun, X.Y.[Xin-Yu], Li, B.[Bing], Lang, C.Y.[Cong-Yan], Zhao, Z.K.[Zhi-Kun], Wang, J.[Juan], Xiong, W.H.[Wei-Hua], Hu, W.M.[Wei-Ming], Cheng, L.[Long],
Reinforcement Learning-Based Sequential Parameter Tuning for Image Signal Processing,
PAMI(48), No. 4, April 2026, pp. 4114-4131.
IEEE DOI 2603
Tuning, Hardware, Artificial intelligence, Pipelines, Optimization, Training data, Reinforcement learning, Image quality, sequential parameter tuning BibRef

Gao, E.[Enting], Zha, Z.[Zian], Li, Y.G.[Yong-Gang], Zhu, J.H.[Jun-Hui], Wang, Y.[Yong], Chen, X.J.[Xin-Jian], Zhou, N.[Naihui], Xiang, D.[Dehui],
Amplitude-guided deep reinforcement learning for semi-supervised layer segmentation,
PR(176), 2026, pp. 113204.
Elsevier DOI 2603
Deep reinforcement learning, Semi-supervised layer segmentation, Cross-power spectrum correlation BibRef

Hu, J.F.[Ji-Feng], Sun, Y.C.[Yan-Chao], Huang, S.[Sili], Guo, S.Y.[Si-Yuan], Chen, H.[Hechang], Shen, L.[Li], Sun, L.C.[Li-Chao], Chang, Y.[Yi], Tao, D.C.[Da-Cheng],
Instructed Diffuser With Temporal Condition Guidance for Offline Reinforcement Learning,
PAMI(48), No. 5, May 2026, pp. 5346-5356.
IEEE DOI 2604
Diffusion models, Decision making, Transformers, Trajectory, Reinforcement learning, Planning, Noise reduction, conditional generation BibRef

Li, T.X.[Tian-Xu], Zhu, K.[Kun],
Efficient Exploration for Multi-Agent Diversity With Agent Identity,
PAMI(48), No. 5, May 2026, pp. 5460-5473.
IEEE DOI 2604
Trajectory, Entropy, Mutual information, Lower bound, Training, Vectors, Reinforcement learning, Diversity methods, Overfitting, agent identity BibRef

Liu, H.C.[Hao-Chen], Li, T.Y.[Tian-Yu], Yang, H.H.[Hao-Han], Chen, L.[Li], Wang, C.[Caojun], Guo, K.[Ke], Tian, H.C.[Hao-Chen], Li, H.C.[Hong-Chen], Li, H.Y.[Hong-Yang], Lv, C.[Chen],
Reinforced Refinement With Self-Aware Expansion for End-to-End Autonomous Driving,
PAMI(48), No. 5, May 2026, pp. 5774-5792.
IEEE DOI 2604
Adaptation models, Self-aware, Autonomous vehicles, Pipelines, Planning, Training, Reinforcement learning, Uncertainty, Data models, motion planning BibRef

Wang, H.[Hao], Li, W.R.[Wen-Rui], Wang, P.H.[Peng-Hong], Zhang, X.Q.[Xian-Qi], Fan, X.P.[Xiao-Peng],
Hierarchical Neural Skill-Based Meta-Reinforcement Learning for Efficient Adaptability in Robotic Manipulation Tasks,
SPLetters(33), 2026, pp. 1741-1745.
IEEE DOI 2605
Feedback, Circuits, Protocols, Communication systems, Local area networks, Data communication, Videos, Message systems, robotic manipulation task BibRef

Xu, M.[Meng], Wen, Z.[Zihao], Chen, X.H.[Xin-Hong], Zhao, G.[Guanyi], Huang, J.[Jin], Wang, J.P.[Jian-Ping],
A Generic Competitive-Cooperative Actor-Critic Framework for Deep Reinforcement Learning,
PAMI(48), No. 6, June 2026, pp. 7112-7128.
IEEE DOI 2605
Estimation, Collaboration, Accuracy, Training, Navigation, Measurement, Limiting, Energy management, Deep reinforcement learning, competition and cooperation BibRef

Liu, K.[Kai], Zhang, T.X.[Tian-Xian], Kong, L.J.[Ling-Jiang], Xu, X.L.[Xiang-Liang],
Value decomposition with maximum correntropy for multi-agent deep reinforcement learning,
PR(178), 2026, pp. 113318.
Elsevier DOI 2605
Value decomposition, Multi-agent deep reinforcement learning, Robust regression, Maximum correntropy criterion BibRef

Ren, J.J.[Jiao-Jiao], Luo, R.[Renfu], Wu, C.[Cong],
Event-Triggered Reinforcement Learning Control for Switching Fuzzy Vehicle Lateral Dynamics,
ITS(27), No. 5, May 2026, pp. 5104-5116.
IEEE DOI 2605
Switches, Vehicle dynamics, Event detection, Reinforcement learning, Control systems, Adaptation models, frequent switching BibRef

Zhang, C.[Chi], Long, T.T.[Ting-Ting], Wang, Z.D.[Zhen-Dong], Xu, L.H.[Lin-Hai], Bi, M.W.[Ming-Wen], Chen, X.Y.[Xing-Yu], Liu, Y.H.[Yue-Hu], Ye, Q.[Qing], Li, L.[Li],
Perception-Failure-Induced Test Scenario Searching via Online Causal Reinforcement Learning,
ITS(27), No. 6, June 2026, pp. 6759-6774.
IEEE DOI 2606
Testing, Safety, Autonomous vehicles, Reinforcement learning, Optical character recognition, Cognition, Standards, Snow, online learning BibRef

Sun, X.[Xiao], Zhu, F.[Fei],
Robust reinforcement learning with State-Driven Dual-Mode Alternating Adversarial Training via temporal bounded attacks,
PR(179), 2026, pp. 113524.
Elsevier DOI 2606
Robust reinforcement learning, Adversarial training, State perturbation, Time-constrained setting, Dual mode adversary BibRef

Zhao, T.[Tianchi], Liu, H.[He], Li, J.[Jing], Liu, Y.C.[Yan-Chao], Shi, H.Y.[Hong-Yin], Zhao, G.Z.[Guang-Zhe], Li, J.L.[Jin-Liang],
Fast online learning algorithm based on modified hierarchical Unimodal Thompson Sampling,
PR(179), 2026, pp. 113523.
Elsevier DOI 2606
MAB, Unimodal bandits, Reinforcement learning BibRef

He, H.Y.[Heng-Yang], Zhang, L.[Le], Zhu, C.[Ce],
GeoRL: Adaptive tokenization via reinforcement learning for remote sensing foundation models,
PR(179), 2026, pp. 113874.
Elsevier DOI 2606
Vision Foundation Models, Adaptive tokenization, Reinforcement learning, Remote sensing, Proximal Policy Optimization BibRef

Mao, Y.[Yudi], Zheng, X.[Xiujuan], Tu, H.Y.[Hai-Yan],
VATE: Variational Attention Trajectory Encoder for Preference-Based Reinforcement Learning,
SPLetters(33), 2026, pp. 2460-2464.
IEEE DOI 2607
Modeling, Reinforcement learning, Labeling, Transformers, Windows, Training, Noise measurement, Learning (artificial intelligence), multi-scale attention aggregation BibRef

Pang, T.[Teng], Wang, B.Z.[Bing-Zheng], Wu, G.Q.[Guo-Qiang], Yin, Y.L.[Yi-Long],
Diffusion classifier-driven reward for offline preference-based reinforcement learning,
PR(180), 2026, pp. 114098.
Elsevier DOI 2608
Offline preference-based reinforcement learning, Reward model, Diffusion model BibRef

Ameur, M.E.[Mohamed El_Amine], Brik, B.[Bouziane], Drias, H.[Habiba], Ameur, M.[Mazene], Foufou, S.[Sebti], Zomaya, A.[Albert],
Deep Reinforcement Learning for Cooperative Intelligent Transportation Systems: A Survey on Architecture, Use Cases, and Future Directions,
ITS(27), No. 8, August 2026, pp. 8960-8981.
IEEE DOI 2608
Modeling, Vehicles, Surveys, Deep reinforcement learning, Roads, Technology, Transportation, Learning (artificial intelligence), vehicle-to-everything (V2X) BibRef

Cao, W.[Wen], Zhang, Y.[Yinbao], Li, R.S.[Run-Sheng], Ren, L.Q.[Li-Qiu], Chen, H.[He],
Progressive Reinforcement Learning for Point-Feature Label Placement in Map Annotation,
IJGI(15), No. 4, 2026, pp. 162.
DOI Link 2605
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Koga, K.C.[Kenji Cari], Kawakami, R.[Rei],
Implicit Object Recognition via Reinforcement Learning in Out-Of-Domain Scenarios,
ICIP25(451-456)
IEEE DOI 2601
Visualization, Adaptation models, Computational modeling, Object detection, Reinforcement learning, Reinforcement Learning BibRef

Babu, A.R.[Ashwin Ramesh], Mousavi, S.[Sajad], Gundecha, V.[Vineet], Ghorbanpour, S.[Sahand], Naug, A.[Avisek], Guillen, A.[Antonio], Gutierrez, R.L.[Ricardo Luna], Sarkar, S.[Soumyendu],
Robustness Evaluation for Video Models with Reinforcement Learning,
SAIAD25(4362-4370)
IEEE DOI 2512
Threat modeling, Measurement, Computational modeling, Perturbation methods, Reinforcement learning, Distortion, Videos BibRef

Hao, C.J.[Chen-Jie], Lu, W.[Weyl], Xu, Y.F.[Yi-Fan], Chen, Y.[Yubei],
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning,
CVPR25(27608-27617)
IEEE DOI 2508
Accuracy, Heuristic algorithms, Dynamics, Zero shot learning, Reinforcement learning, Transforms, Predictive models, neural ODE BibRef

Croitoru, F.A.[Florinel-Alin], Hondru, V.[Vlad], Ionescu, R.T.[Radu Tudor], Sebe, N.[Nicu], Shah, M.[Mubarak],
Curriculum Direct Preference Optimization for Diffusion and Consistency Models,
CVPR25(2824-2834)
IEEE DOI Code:
WWW Link. 2508
Training, Measurement, Text to image, Reinforcement learning, Performance gain, Diffusion models, Numerical models, Optimization, consistencu models BibRef

Sengupta, K.[Kathakoli], Shangguan, Z.K.[Zhong-Kai], Bharadwaj, S.[Sandesh], Arora, S.[Sanjay], Ohn-Bar, E.[Eshed], Mancuso, R.[Renato],
Unified Local-cloud Decision-making via Reinforcement Learning,
ECCV24(XLI: 185-203).
Springer DOI 2412
BibRef

Li, Q.F.[Qi-Feng], Jia, X.S.[Xiao-Song], Wang, S.B.[Shao-Bo], Yan, J.C.[Jun-Chi],
Think2drive: Efficient Reinforcement Learning by Thinking with Latent World Model for Autonomous Driving (in Carla-v2),
ECCV24(XLV: 142-158).
Springer DOI 2412
BibRef

Zhang, Y.[Yinan], Tzeng, E.[Eric], Du, Y.L.[Yi-Lun], Kislyuk, D.[Dmitry],
Large-scale Reinforcement Learning for Diffusion Models,
ECCV24(LXIII: 1-17).
Springer DOI 2412
BibRef

Guo, T.[Taian], Zhang, T.[Taolin], Wu, H.Q.[Hao-Qian], Li, H.[Hanjun], Qiao, R.Z.[Rui-Zhi], Sun, X.[Xing],
Multimodal Label Relevance Ranking via Reinforcement Learning,
ECCV24(LXVI: 391-408).
Springer DOI 2412
BibRef

Tran, T.[Tung], Than, K.[Khoat], Vargas, D.[Danilo],
Robust Visual Reinforcement Learning by Prompt Tuning,
ACCV24(IX: 387-401).
Springer DOI 2412
BibRef

Zhang, Y.X.[Yuan-Xin], Ma, H.M.[Hui-Min], Wang, Y.[Yu],
AVD-Net: Attention Value Decomposition Network For Deep Multi-Agent Reinforcement Learning,
ICPR21(7810-7816)
IEEE DOI 2105
Training, Scalability, Reinforcement learning, Games, Markov processes, Machine translation 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.C.[Ming-Chen], 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.K.[Ding-Kang], 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
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Xie, Y.S.[Yuan-Sheng], Vosoughi, S.[Soroush], Hassanpour, S.[Saeed],
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning,
ICPR22(5067-5074)
IEEE DOI 2212
Deep learning, Atmospheric modeling, Computational modeling, Decision making, Reinforcement learning, Abstractive Summarization 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 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 .