Gottesman, O. et al. Guidelines for reinforcement learning in healthcare. Nat. Med. 25(1), 16–18 (2019).
Rocchetta, R., Bellani, L., Compare, M., Zio, E. & Patelli, E. A reinforcement learning framework for optimal operation and maintenance of power grids. Appl. Energy 241, 291–301 (2019).
Talaat, F. M., Saraya, M. S., Saleh, A. I., Ali, H. A. & Ali, S. H. A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment. J. Ambient Intell. Humaniz. Comput. 11, 4951–4966 (2020).
Negarestani, M. et al. Design and preparation of magnetic bio-surfactant rhamnolipid-layered double hydroxide nanocomposite as an efficient and recyclable adsorbent for the removal of Rifampin from aqueous solution. Sep. Purif. Technol. 304, 122362 (2023).
Zhang, C., Gupta, C., Farahat, A., Ristovski, K., & Ghosh, D. “Equipment health indicator learning using deep reinforcement learning.” In Machine Learning and Knowledge Discovery in Databases , 488-504. Cham: Springer International Publishing. (2019)
Fan, Y., Xue, K., Li, Z., Zhang, X., & Ruan, T. “An LLM-based framework for biomedical terminology normalization in social media via multi-agent collaboration.” In Proceedings of the 31st International Conference on Computational Linguistics , 10712-10726. (2025)
Gabay, Y. Delaying feedback compensates for impaired reinforcement learning in developmental dyslexia. Neurobiol. Learn Mem. 185, 107518 (2021).
Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nat. Mach. Intell. 1(3), 133–143 (2019).
Recht, B. A tour of reinforcement learning: The view from continuous control. Annu. Rev. Control Robot. Auton. Syst. 2, 253–279 (2019).
Ullah, I. et al. Multi-agent reinforce- ment learning for task allocation in the internet of vehicles: Exploring benefits and paving the future. Swarm Evol. Comput. 94, 101878 (2025).
Wu, J., Zhang, N., Li, D., Bi, J. & Han, G. A context-aware feature fusion method for multi-UAV cooperative air combat. IEEE Trans. Intell. Transp. Syst. https://doi.org/10.1109/tits.2025.3530463 (2025).
Kane, D., Liu, S., Lovett, S., & Mahajan, G. “Computational-statistical gap in reinforcement learning.” In Conference on Learning Theory (pp. 1282-1302). PMLR. (2022)
Swarnamugi, M., & Chinnaiyan, R. “Modelling and reasoning techniques for context- aware computing in intelligent transportation system.” arXiv:2107.14374 .(2021)
Sarker, I. H. Context-aware rule learning from smartphone data: Survey, challenges and future directions. J. Big Data 6(1), 1–25 (2019).
Leiz, M., Pfeuffer, N., Rehner, L., Stentzel, U. & van den Berg, N. Telemedicine as a tool to improve medicine adherence in patients with affective disorders–A systematic literature review. Patient Prefer. Adherence https://doi.org/10.2147/ppa.s388106 (2022).
Yu, C., Liu, J., Nemati, S. & Yin, G. Reinforcement learning in healthcare: A survey. ACM Comput. Surveys (CSUR) 55(1), 1–36 (2021).
Afsar, M. M., Crump, T. & Far, B. Reinforcement learning based recommender systems: A survey. ACM Comput. Surv. https://doi.org/10.1145/3543846 (2022).
Gil, C. R., Calvo, H. & Sossa, H. Learning an efficient gait cycle of a biped robot based on reinforcement learning and artificial neural networks. Appl. Sci. 9(3), 502 (2019).
Alkhodari, M. et al. The role of artificial intelligence in hypertensive disorders of pregnancy: Towards personalized healthcare. Expert Rev. Cardiovasc. Ther. https://doi.org/10.1080/14779072.2023.2223978 (2023).
Soder, H. E. et al. Dose-response effects of d-amphetamine on effort-based decision- making and reinforcement learning. Neuropsychopharmacology 46(6), 1078–1085 (2021).
Li, F. et al. Harnessing artificial intelligence in sepsis care: Advances in early detection, personalized treatment, and real-time monitoring. Front. Med. 11, 1510792 (2025).
Chkirbene, Z., Abdellatif, A. A., Mohamed, A., Erbad, A. & Guizani, M. Deep reinforce- ment learning for network selection over heterogeneous health systems. IEEE Trans. Netw. Sci. Eng. 9(1), 258–270 (2021).
Wang, Y., & Zeng, Y. “A brain-inspired computational model for human-like concept learning.” arXiv preprint , arXiv:2401.06471. (2024)
Abdullah, K., Siddique, A., Fatima, Z. & Shaukat, K. Traumatic brain injury structure detection Us- ing advanced wavelet transformation fusion algorithm with proposed CNN-ViT… Information 15, 612. https://doi.org/10.3390/info15100612 (2024).
Wang, J. Multi-agent system based smart grid anomaly detection using blockchain machine learning model in mobile edge computing network.. Comput. Electr. Eng. 121, 109825 (2025).
Ye, Z., Gao, Y., Xiao, Y., Xiong, Z., & Niyato, D Deep reinforcement learning empowered activity-aware dynamic health monitoring systems. In ICC 2024-IEEE International Conference on Communications (pp. 2155-2160). IEEE. (2024)
Alahi, M. E. E. et al. Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: Recent advancements and future trends.. Sensors 23(11), 5206 (2023).
Lee, J. S., Mahendra, M., & Aswani, A. Methodology for interpretable reinforcement learning for optimizing mechanical ventilation. arXiv preprint arXiv:2404.03105. (2024)
Xu, H. et al. Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement. Urban. Inf. 3(1), 29 (2024).
Shaik, T. et al. Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. WIREs Data Min. Knowl. Discov. 13(2), e1485 (2023).
Nguyen, H., Nawara, D. & Kashef, R. Connecting the indispensable roles of IoT and artificial intelligence in smart cities: A survey. J. Inform. Intell. 2(3), 261–285 (2024).
Takita, H. et al. A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians. npj Digit. Med. 8(1), 175 (2025).
Li, S. E. “Model-Free Indirect RL: Temporal Difference.” In Reinforcement Learning for Sequential Decision and Optimal Control (pp. 67-87). Springer. (2023)
Alahi, M. E. E. et al. Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: Recent advancements and future trends. Sensors 23, 5206. https://doi.org/10.3390/s23115206 (2023).
Schuler, K. et al. Context factors in clinical decision-making: a scoping review. BMC. Med. Inform. Decis. Mak. 25(1), 133 (2025).
Shaik, T., Tao, X., Li, L., Xie, H., Dai, H. N., Zhao, F., & Yong, J. Adaptive multi-agent deep reinforcement learning for timely healthcare interventions. arXiv preprint arXiv:2309.10980. (2023)
Toor, R. & Chana, I. DAPNEML: Disease-diet associations prediction in a network using a machine learning-based approach.. J. Netw. Comput. Appl. https://doi.org/10.1016/j.jnca.2025.104140 (2025).
Maleh, Y., Abd El-Latif, A. A., Curran, K., & Siarry, P. (Eds.). Computational intelligence for medical internet of things (MIoT) applications: machine intelligence applications for IoT in Healthcare . Elsevier. (2023)
Abdullah, K., Siddique, A., Shaukat, K. & Jan, T. An intelligent mechanism to detect multi-factor skin cancer.. Diagnostics 14, 1359. https://doi.org/10.3390/diagnostics14131359 (2024).
David Blumenthal et al., Mirror, Mirror 2024: A Portrait of the Failing U.S. Health System — Comparing Performance in 10 Nations (Commonwealth Fund, Sept. 2024). https://doi.org/10.26099/ta0g-zp66
Agarwal, H. & Rathore, H. BGRL: Basal Ganglia-inspired reinforcement learning-based framework for deep brain stimulators. Artif. Intell. Med. 147, 102736 (2024).
Cuk, A. et al. Tuning attention based long-short term memory neural networks for Parkinson’s disease detection using modified metaheuristics. Sci. Rep. 14(1), 4309 (2024).
Shah, S. P. & Heiss, J. D. Artificial intelligence as a complementary tool for clincal decision-making in stroke and epilepsy. Brain Sciences 14, 228. https://doi.org/10.3390/brainsci14030228 (2024).
Padmanabhan, R., Meskin, N., Khattab, T., Shraim, M. & Al-Hitmi, M. Reinforcement learning-based decision support system for COVID-19. Biomed. Signal Process. Control 68, 102676 (2021).
Sethi, K., Sai Rupesh, E., Kumar, R., Bera, P. & Madhav, Y. V. A context-aware robust intrusion detection system: A reinforcement learning-based approach. Int. J. Inf. Secur. 19, 657–678 (2020).
Naeem, M., Paragliola, G. & Coronato, A. A reinforcement learning and deep learning based intelligent system for the support of impaired patients in home treatment. Expert Syst. Appl. 168, 114285 (2021).
Abdellatif, A. A., Mohamed, A., Chiasserini, C. F., Tlili, M. & Erbad, A. Edge computing for smart health: Context-aware approaches, opportunities, and challenges. IEEE Netw. 33(3), 196–203 (2019).
Casmin, E. & Oliveira, R. Survey on context-aware radio frequency-based sensing. Sensors (Basel) 25(3), 602 (2025).
Siu, H. C. et al. Evaluation of human-AI teams for learned and rule-based agents in Hanabi. Adv. Neural Inf. Process. Syst. 34, 16183–16195 (2021).
Chen, G., Li, D., & Xu, R. “Context-aware active multi-step reinforcement learning.” arXiv preprint arXiv:1911.04107 . (2019)
Kwak, G. H., Ling, L. & Hui, P. Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic. PLoS One 16(5), e0251550 (2021).
P. Kumar et al., “Integrating Convolutional and Transformer Networks for Efficient Retinal Disease Screening,” In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Tirupur, India, 1979–1984, https://doi.org/10.1109/ICIMIA67127.2025.11200598. (2025)
Rahman, M. A. et al. An intelligent brain tumor detection model using Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images. J. X-Ray Sci. Technol. 33(3), 540–552 (2025).
T. S. et al., “Innovative image processing methods for lung cancer detection and stage prediction,” In 2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS) 1–6 https://doi.org/10.1109/ICETAS.2024.xxxxxxx (if available, otherwise leave as is). (2024)
Wang, H. et al. An intelligent brain tumor detection model using lightweight hybrid twin attentive pyramid convolutional network. Sci. Rep. 15, 40177. https://doi.org/10.1038/s41598-025-23813-2 (2025).
M. F. Tørring, A. Logacjov, A. Ustad, S. M. Brændvik, K. Roeleveld, and E. M. Bardal, “NTNU‑HARChildren for the Validation of HAR‑models for typically developing children and children with Cerebral Palsy,” DataverseNO, V1, 2024. https://doi.org/10.18710/EPCXCC.
J. Ailsworth, “EEG-EMG Hand Exoskeleton,” Mendeley Data, V1 https://doi.org/10.17632/8x4vkhy753.1. (2025)
J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine, “D4RL: Datasets for deep data‑driven reinforcement learning,” arXiv preprint, arXiv:2004.07219, (2020).
Chebotar, Y., Hausman, K., Zhang, M., Sukhatme, G., Schaal, S., & Levine, S. Combining model-based and model-free updates for trajectory-centric reinforcement learning. In International conference on machine learning (pp. 703-711). PMLR. (2017)
Wang, C., Zhao, X. & Guo, X. Weighted dueling double deep Q-network. Nanjing Xinxi Gongcheng Daxue Xuebao 13(5), 564–570 (2021).
Coronato, A., Naeem, M., De Pietro, G. & Paragliola, G. Reinforcement learning for intelligent healthcare applications: A survey. Artif. Intell. Med. 109, 101964 (2020).
Foulds, J., Witten, I. H., Frank, E., Hall, M. A. & Pal, C. J. Data Mining: Practical machine learning tools and techniques (Elsevier, 2025).
Abid, N.”Synergizing AI for cognitive insights, visual pattern recognition, and compu- tational advancements: a novel exploration of EEG detection, deep learning, and cat swarm optimization.” (2025)
Ranjith, J., & Baskaran, S. “A comprehensive survey of memory update mechanisms for continual learning on text datasets.” Turkish Journal of Computer and Mathematics Education (TURCOMAT) ISSN 3048 4855. (2025)
Subramanian, A., Palanichamy, N., Ng, K. W. & Aneja, S. Climate change analysis in Malaysia using machine learning. J. Inform. Web Eng. 4(1), 307–319 (2025).
Makondo, N., Folarin, A. L., Zitha, S. N., & Remy, S. L. “An analysis of reinforcement learning for malaria control.” arXiv preprint arXiv:2107.08988 .(2021)
Ge, Q., et al. “Recurrent neural reinforcement learning for counterfactual evaluation of public health interventions on the spread of covid-19 in the world.” medRxiv .(2020)
Yi, L. et al. Reinforcement-learning-enabled partial confident information coverage for IoT-based bridge structural health monitoring. IEEE Internet Things J. 8(5), 3108–3119 (2020).
Xu, M., Yang, X., Liang, W., Zhang, C., & Zhu, Y. “Learning to plan with personalized preferences.” In arXiv preprint arXiv:2502.00858 .(2025)
Tidd, B., Cosgun, A., Leitner, J., & Hudson, N. “Passing through narrow gaps with deep reinforcement learning.” In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3492-3498). IEEE. (2021)
Wauthier, S. T., Mazzaglia, P., Catal, O., De Boom, C., Verbelen, T., & Dhoedt, B. “A learning gap between neuroscience and reinforcement learning.” arXiv preprint arXiv:2104.10995 .(2021)
Yang, Z. et al. Incremental model-based reinforcement learning with model constraint. Neural Netw. https://doi.org/10.1016/j.neunet.2025.107245 (2025).
Li, Z., Gong, J., Xiong, X. & Wang, D. Multi-slot secure offloading and resource management in VEC networks: A deep reinforcement learning-based method. IEEE Access https://doi.org/10.1109/access.2024.3524636 (2025).
Khan, D. A. “Investigating reward shaping, curiosity, and synchronization in A3C and their alignment with neural reinforcement learning pathways.” (2025)
Langdon, A. et al. Meta- learning, social cognition, and consciousness in brains and machines. Neural Netw. 145, 80–89 (2022).
Chung, K. T., Lee, C. K. M. & Tsang, Y. P. Neural combinatorial optimization with reinforcement learning in industrial engineering: A survey… Artif. Intell. Rev. 58(5), 130 (2025).
Nam, K., Heo, S. & Yoo, C. Multi-agent reinforcement learning-driven adaptive con- troller tuning system for autonomous control of wastewater treatment plants: An offline learning approach. J. Water Process Eng. 70, 107059 (2025).
Tsang, C. C. S. & Wang, J. Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning. Expert Rev. Pharmacoecon. Outcomes Res. 25(2), 187–195 (2025).
Liu, S., Ngiam, K. Y., & Feng, M. “Deep reinforcement learning for clinical decision support: a brief survey.” arXiv preprint arXiv:1907.09475 .(2019)
Grimm, V. et al. Using the ODD protocol and NetLogo to replicate agent-based models. Ecol. Model. 501, 110967 (2025).
Zai, A. T., Lorenz, C., Srikantharajah, S., Giret, N., & Hahnloser, R. H “Estimating the motor exploration in reinforcement learning.” In bioRxiv , 2025-01. (2025)
Lei, L. et al. Deep reinforcement learning for autonomous Internet of Things: Model, applications, and challenges. IEEE Commun. Surv. Tutor. 22(3), 1722–1760 (2020).
Hargrave, M., Spaeth, A. & Grosenick, L. EpiCare: A reinforcement learning benchmark for dynamic treatment regimes. Adv. Neural Inf. Process. Syst. 37, 130536–130568 (2025).
Liu et al., 2022. EZ-Vent: Reinforcement Learning for Mechanical Ventilation Settings.
Petersen, B. K. et al. Deep reinforcement learning and simulation as a path toward precision medicine. J. Comput. Biol. 26(6), 597–604 (2019).
Eghbali, N., Alhanai, T., & Ghassemi, M. M. Reinforcement learning approach to sedation and delirium management in the intensive care unit. In 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (pp. 1-5). I (2023)
Wang, G. et al. Optimized glycemic control of type 2 diabetes with reinforcement learning: A proof-of-concept trial. Nat. Med. 29(10), 2633–2642 (2023).
Watts, J., Khojandi, A., Vasudevan, R., & Ramdhani, R. Optimizing individualized treatment planning for Parkinson’s disease using deep reinforcement learning. In 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC) (pp. 5406-5409). IEEE (2020)
R. Karthikeyan et al., “Game Therapy for Specially‑Abled Individuals with PPO Reinforcement Learning in VR‑based Educational Games,” In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), Tirunelveli, India 1126–1133, https://doi.org/10.1109/ICDICI62993.2024.10810847. (2024)
Sharma, A. et al. Reinforcement learning-based AI assistant and VR play therapy game for children with Down syndrome bound to wheelchairs. AIMS. Math. 8(7), 16989–17011. https://doi.org/10.3934/math.2023867 (2023).
M. Al‑Maitah et al., “comprehensive evaluation of federated learning based models for disease detection in healthcare,” In 2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Sakhir, Bahrain 643–650, https://doi.org/10.1109/3ict64318.2024.10824463. (2024)
S. Gupta et al., “An AI‑based framework for real‑time patient monitoring and intelligent treatment recommendation in critical care units,” In 2025 2nd International Conference on Computing and Data Science (ICCDS), Chennai, India, 1–5, https://doi.org/10.1109/ICCDS64403.2025.11209640. (2025)
L. N. et al., “intelligent multi‑agent reinforcement learning based disease prediction and treatment recommendation model,” in 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India 216–221, https://doi.org/10.1109/ICAISS55157.2022.10010747. (2022)
Wang, X. & Ye, X. Consciousness-driven reinforcement learning: An online learning control framework. Int. J. Intell. Syst. 37(1), 770–798 (2022).