An Introduction to Deep Reinforcement Learning

Automatic Disease Diagnosis System Using Deep Q-Network Reinforcement Learning

Baker, Q. B., Swedat, S. A., & Aleesa, K. (2023) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/icics60529.2023.10330536 · Cited by 5

Role of Artificial Intelligence in Periodontology

Moosa, Y., Bacha, S. H., Raza, S. A., Zia, M. H., Fatima, A., & Shaikh, A. A. (2023) · Pakistan Journal of Medical and Health Sciences · doi:10.53350/pjmhs2023175363 · Cited by 5

A Lunar Robot Obstacle Avoidance Planning Method Using Deep Reinforcement Learning for Data Fusion

Hu, R., & Wang, Z. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8997266 · Cited by 5

Improving Positioning Accuracy of an Articulated Robot Using Deep Q-Learning Algorithms

Petronis, A., Bučinskas, V., Šumanas, M., Dzedzickis, A., Petrauskas, L., Sitiajev, N. E., & Morkvėnaitė-Vilkončienė, I. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-40971-5_24 · Cited by 5

Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles

Nassef, O., Sequeira, L., Salam, E., & Mahmoodi, T. (2020) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc48278.2020.9217273 · Cited by 5

Quadruped Robot Locomotion in Unknown Terrain Using Deep Reinforcement Learning

Pei, M., Wu, D., & Wang, C. (2020) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus50048.2020.9274920 · Cited by 6

Procedure model for the development and launch of intelligent assistance systems

Reichardt, P., Lang, S., & Reggelin, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.01.348 · Cited by 5

Generic Itemset Mining Based on Reinforcement Learning

Fujioka, K., & Shirahama, K. (2022) · IEEE Access · doi:10.1109/access.2022.3141806 · Cited by 5

Towards End-to-End Chase in Urban Autonomous Driving Using Reinforcement Learning

Kołomański, M., Sakhai, M., Nowak, J., & Wielgosz, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16075-2_29 · Cited by 5

Adversarial Attacks on Heterogeneous Multi-Agent Deep Reinforcement Learning System with Time-Delayed Data Transmission

Fard, N. E., & Šelmić, R. R. (2022) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan11030045 · Cited by 5

PigSNIPE: Scalable Neuroimaging Processing Engine for Minipig MRI

Brzus, M., Knoernschild, K., Sieren, J. C., & Johnson, H. J. (2023) · Algorithms · doi:10.3390/a16020116 · Cited by 5

An Invitation to Deep Reinforcement Learning

Jaeger, B., & Geiger, A. (2024) · Foundations and Trends® in Optimization · doi:10.1561/2400000049 · Cited by 5

An analytical diabolo model for robotic learning and control

von Drigalski, F., Joshi, D. O., Murooka, T., Tanaka, K., Hamaya, M., & Ijiri, Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561578 · Cited by 5

Linear Representation Meta-Reinforcement Learning for Instant AdaptationPreprint

Peng, M., Zhu, B., & Jiao, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04750 · Cited by 5

Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning.Preprint

Gomrokchi, M., Amin, S., Aboutalebi, H., Wong, A. K. C., & Precup, D. (2021) · arXiv (Cornell University) · Cited by 5

Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning ApproachPreprint

Pinto-Ríos, J., Calderón, F., Leiva, A., Hermosilla, G., Beghelli, A., Bórquez‐Paredes, D., Lozada, A., Jara, N., Olivares, R., & Saavedra, G. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4075565 · Cited by 8

Deep Reinforcement Learning for Intrusion Detection in Internet of Things: Best Practices, Lessons Learnt, and Open ChallengesPreprint

Moreno, J. F. C., Rizzardi, A., Sicari, S., & Coen‐Porisini, A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4482146 · Cited by 5

Real-Time Optimal Route Planning by Deep Reinforcement Learning and Validation with Flight Test

Shim, J., Park, J., Song, N. C., Jang, J., Choi, J.-Y., Lee, G., Prasad, R., & Choi, S. (2023) · AIAA AVIATION Forum · doi:10.2514/6.2023-3623 · Cited by 4

Adaptive Optimization Operation of Electric Vehicle Energy Replenishment Stations Considering the Degradation of Energy Storage Batteries

Bai, Y., & Zhu, Y. (2023) · Energies · doi:10.3390/en16134879 · Cited by 4

Twin Delayed DDPG based Dynamic Power Allocation for Internet of Robotic Things

Kabir, H., Tham, M., & Chang, Y. C. (2022) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom55329.2022.9911277 · Cited by 4

Offline Reinforcement Learning via Policy Regularization and Ensemble Q-Functions

Wang, T., Xie, S., Gao, M., Chen, X., Zhang, Z., & Yu, H. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00178 · Cited by 4

Advanced Seismic Magnitude Classification Through Convolutional and Reinforcement Learning Techniques

Lin, Q., & Li, J. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0141118 · Cited by 4

IGSA-SAC: a novel approach for intrusion detection using improved gravitational search algorithm and soft actor-critic

Jin, L., Fan, R., Han, X., & Cui, X. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1574211 · Cited by 5

Constrained-optimization in a 3D bin packing realistic problem

Rodriguez, Y. M., López, J. C. D., & Jimenez, J. A. (2021) · International Young Engineers Forum (YEF-ECE) · doi:10.1109/yef-ece52297.2021.9505150 · Cited by 4

Joint Mode Selection And Power Allocation for NOMA Systems With D2D Communication

Tang, R., Zhang, R., Xia, Y., Zhao, Y., He, J., & Long, Y. (2021) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc52777.2021.9580380 · Cited by 4

Virtual Power Plant Adjustable Resource Aggregation Adjustment Optimization Strategy Based on Multi-agent Game

Que, L., Jiang, X., Wang, B., Jin, X., Cai, Z., Shi, L., & Lyn, Q. (2021) · International Conference on Networking, Communications and Information Technology (NetCIT) · doi:10.1109/netcit54147.2021.00064 · Cited by 4

Time-delayed Data Transmission in Heterogeneous Multi-agent Deep Reinforcement Learning System

Fard, N. E., & Šelmić, R. R. (2022) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med54222.2022.9837194 · Cited by 4

Hierarchical Driving Strategy for Connected and Autonomous Vehicles Making a Protected Left Turn at Signalized Intersections

Du, D., Shen, M., Guo, X., & Sun, C. (2022) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-7243 · Cited by 4

Efficient Multi-Task Learning via Iterated Single-Task Transfer

Zentner, K. R., Puri, U., Zhang, Y., Julian, R., & Sukhatme, G. S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981244 · Cited by 5

Online Hybrid Learning to Speed Up Deep Reinforcement Learning Method for Commercial Aircraft Control

Xin, M., Gao, Y., Mou, T., & Ye, J. (2019) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isass.2019.8757756 · Cited by 4

Reinforcement Learning - A Technical Introduction

Diederichs, E. (2019) · Journal of Autonomous Intelligence · doi:10.32629/jai.v2i2.45 · Cited by 8

Quasi-Newton Iteration in Deterministic Policy Gradient

Kordabad, A. B., Esfahani, H. N., Cai, W., & Gros, S. (2022) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc53348.2022.9867217 · Cited by 4

Curriculum based Reinforcement Learning for traffic simulations

Makri, S., & Charalambous, P. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.04.009 · Cited by 4

Deep Reinforcement Learning for Cost-Effective Controller Placement in Software-Defined Multihop Wireless Networking

Zahmatkesh, A., & Lung, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98978-1_9 · Cited by 3

AI-Enabled Soft Versatile Grasping for High-Mixed-Low-Volume Applications with Tactile Feedbacks

Xie, Z., Seng, J. C. Y., & Lim, G. (2022) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac55051.2022.9911143 · Cited by 3

Applied Reinforcement Learning Method for Pressure Control in Managed Pressure Drilling

Usama, M., Cao, J., Gravdal, J. E., & Sui, D. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10239724 · Cited by 3

Boosting vehicular connectivity through resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization

Zhao, J., Xiong, X., Zhang, Q., Ren, S., Chen, J., Xu, W., & Wang, D. (2024) · Vehicular Communications · doi:10.1016/j.vehcom.2024.100856 · Cited by 3

Multi‐agent track confirmation utilising reinforcement learning and game theoretics

Dolinger, G., Sharp, T., Lavender, B., Stringer, A., Karch, J., Bowersox, A., & Metcalf, J. G. (2025) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.12694 · Cited by 3

Deep Recurrent Deterministic Policy Gradient for Physical Control

Zhang, L., Han, S. D., Zhang, Z., Li, L., & Lü, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61616-8_21 · Cited by 3

CIExplore

Yang, H., Shi, D., Zhao, C., Xie, G., & Yang, S. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482326 · Cited by 3

Application of Improved DQN Algorithm in Three-Dimensional Garage Scheduling

Yi, G., Li, S., Zhou, W., & Chen, Y. (2021) · International Conference on Robotics, Control and Automation Engineering (RCAE) · doi:10.1109/rcae53607.2021.9638792 · Cited by 3

Solving problems of the oil and gas sector using machine learning algorithms

(2021) · Acta Montanistica Slovaca · doi:10.46544/ams.v26i2.11 · Cited by 3

PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services

Qiu, M., Najm, E., Sharrock, R., & Traverson, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12426-6_13 · Cited by 3

Multi-Timeframe Algorithmic Trading Bots Using Thick Data Heuristics with Deep Reinforcement Learning

Roy, G., Fiaidhi, J., & Mohammed, S. (2022) · Artificial Intelligence Evolution · doi:10.37256/aie.3220221722 · Cited by 5

How generalization relates to the exploration-exploitation tradeoff

Houser, T. M. (2023) · Frontiers in Cognition · doi:10.3389/fcogn.2023.1132766 · Cited by 3

Deep Learning Neural Networks in the Cloud

Awan, B. H. (2023) · International Journal of Advanced engineering Management and Science · doi:10.22161/ijaems.910.2 · Cited by 3

Deep Neural Networks and Experience Replay in Q-learning Extensions: A Review

Osei, R. S., & Lopez, D. (2025) · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488525500175 · Cited by 4

Dynamic Resource Management in Next Generation Networks based on Deep Q Learning

Aslan, A., Bal, G., & Toker, C. (2020) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu49456.2020.9302152 · Cited by 3

Towards a multi-agent non-player character road network: a Reinforcement Learning approach

Makri, S., & Charalambous, P. (2021) · IEEE Conference on Games (CoG) · doi:10.1109/cog52621.2021.9619047 · Cited by 3

A DRL-Based Decision Support System for Voltage-Reactive Power Stability in Grids with Aggregated Inverter-based Resources

Olowolaju, J., & Livani, H. (2022) · IEEE Kansas Power and Energy Conference (KPEC) · doi:10.1109/kpec54747.2022.9814768 · Cited by 2

A Multi-agent Design of a Computer Player for Nine Men's Morris Board Game using Deep Reinforcement Learning

Abukhait, J. J., Aljaafreh, A., & Al-Oudat, N. (2019) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams.2019.8931879 · Cited by 3

A Reinforcement Learning Controller for the Swing-Up of the Furuta Pendulum

Guida, D., Manrique-Escobar, C. A., & Pappalardo, C. M. (2020) · Lecture notes in networks and systems · doi:10.1007/978-3-030-46817-0_3 · Cited by 3

Modeling oscillatory car following using deep reinforcement learning based car following models

Nguyen, J., & Stern, R. (2021) · International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) · doi:10.1109/mt-its49943.2021.9529262 · Cited by 3

Prehensile Robotic pick-and-place in clutter with Deep Reinforcement Learning

Imtiaz, M. B., Qiao, Y., & Lee, B. (2022) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet55527.2022.9873426 · Cited by 3

A Multi-Agent Reinforcement Learning-Based Approach for UAV-Assisted Vehicle-to-Everything Network

Jawad, A. T., Mâaloul, R., & Fourati, L. C. (2023) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit58514.2023.10284263 · Cited by 3

One-time learning in a biologically-inspired Salience-affected Artificial Neural Network (SANN)Preprint

Remmelzwaal, L. A., Ellis, G., & Tapson, J. C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/726331 · Cited by 3

Resource Allocation Using Gradient Boosting Aided Deep Q-Network for IoT in C-RANsPreprint

Luo, Y., Yang, J., Xu, W., Wang, K., & Renzo, M. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.13084 · Cited by 3

An Intelligent Traffic Classification Based Optimized Routing in SDN-IoT: A Machine Learning Approach

Ampratwum, I. (2020) · uO Research (University of Ottawa) · doi:10.20381/ruor-24389 · Cited by 3

QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningPreprint

Leroy, P. L., Ernst, D., Geurts, P., Louppe, G., Pisane, J., & Sabatelli, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12062 · Cited by 7

A Sufficient Statistic for Influence in Structured Multiagent EnvironmentsPreprint

Oliehoek, F. A., Witwicki, S., & Kaelbling, L. P. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5

Admission Control for 5G Network Slicing based on (Deep) Reinforcement LearningPreprint

Jácome, W. F. V., Rendón, O. M. C., & da Fonseca, N. L. S. (2021) · doi:10.36227/techrxiv.14498190.v1 · Cited by 3

Deep Reinforcement Learning in Human Activity Recognition: A SurveyPreprint

Nikpour, B., Sinodinos, D., & Armanfard, N. (2022) · doi:10.36227/techrxiv.19172369 · Cited by 3

Learning to Navigate Through Reinforcement Across the Sim2Real GapPreprint

Azzam, R., Chehadeh, M. S., Hay, O. A., Boiko, I. M., & Zweiri, Y. (2022) · doi:10.36227/techrxiv.20138960.v1 · Cited by 3

Reinforcement Learning for Multi-Function Radar Resource Management

Vincent, M. (2023) · theses.fr (ABES) · doi:10.70675/945956b6z2536z4cc4za84czf6468b84a887 · Cited by 3

BO-RL: Buffer Optimization in Data Plane Using Reinforcement Learning

Sneha, M., Kataoka, K., & Shobha, G. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-75100-5_31 · Cited by 2

Morphognostic Honey Bees Communicating Nectar Location Through Dance Movements

Portegys, T. E. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27524-1_72 · Cited by 3

Methoden des bestärkenden Lernens für die Produktionsablaufplanung

Lang, S. (2023) · Springer eBooks · doi:10.1007/978-3-658-41751-2 · Cited by 1

Dynamic Pricing Strategy in Electricity Trading Market Based on Reinforcement Learning

Hu, C., & An, D. (2023) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc58219.2023.10327004 · Cited by 2

A Reinforcement Learning Approach for Mobile Beamforming

Dimas, A., Diamantaras, K., & Petropulu, A. P. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048888 · Cited by 2

Evaluating skills in hierarchical reinforcement learning

Farahani, M. D., & Mozayani, N. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01141-3 · Cited by 2

Reinforcement Learning Applied to Hexapod Robot Locomotion: An Overview

Jorge, E., Gorrostieta-Hurtado, E., Emilio, V.-S., & Ramos-Arreguín, J. M. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-62554-2_14 · Cited by 2

Redefining Neural Architecture Search of Heterogeneous Multinetwork Models by Characterizing Variation Operators and Model Components

Garciarena, U., Santana, R., & Mendiburu, A. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3242877 · Cited by 2

Joint beamforming and user association with reduced CSI signaling in mobile environments: A Deep Q-learning approach

Ha, D. T., Boukhatem, L., Kaneko, M., & Nguyen‐Thanh, N. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108291 · Cited by 2

Deep Q-learning for Control: Technique and Implementation Considerations on a Physical System: Active Automotive Rear Spoiler Case

Miguel, V. E. V., Angel, G. G. J., Omar, M., Ponce, P., & Molina, A. (2021) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma52036.2021.9512669 · Cited by 2

Reinforcement Learning: Beyond the Basal Ganglia

Yuan, C., & Fathi, M. (2022) · Educational communications and technology: issues and innovations · doi:10.1007/978-3-030-84729-6_16 · Cited by 2

Robot Obstacle Avoidance Controller Based on Deep Reinforcement Learning

Tang, Y., Chen, Q., & Wei, Y. (2022) · Journal of Sensors · doi:10.1155/2022/4194747 · Cited by 3

Dynamic Pricing Integrated Demand Response for Multiple Energy Carriers with Deep Reinforcement Learning

Almannouny, G., Bu, S., & Yang, J. S. (2022) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgt-europe54678.2022.9960326 · Cited by 2

Relevance vector machine (RVM)

Jabbari, M. R., Dorafshan, M. M., & Eslamian, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821285-1.00008-7 · Cited by 2

Throughput maximization of EH-CRN-NOMA based on PPO

Du, K., Xie, X., Shi, Z., & Li, M. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10133954 · Cited by 2

Benchmarking Unsupervised Representation Learning for Continuous Control

Antonova, R., Devlin, S., Hofmann, K., & Kragić, D. (2020) · Cited by 2

Learning Pick to Place Objects using Self-supervised Learning with Minimal Training Resources

Mohammed, M. Q., Kwek, L. C., & Chua, S. C. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0121056 · Cited by 2

M-DRL: Deep Reinforcement Learning Based Coflow Traffic Scheduler with MLFQ Threshold Adaption

Chen, T., Li, W., Sun, Y., & Li, Y. (2021) · International Journal of Parallel Programming · doi:10.1007/s10766-021-00711-4 · Cited by 2

Deriving Fast AC OPF Solutions via Proximal Policy Optimization for Secure and Economic Grid Operation.Preprint

Zhou, Y., Zhang, B., Xu, C., Lan, T., Diao, R., Shi, D., Wang, Z., & Lee, W. (2020) · arXiv (Cornell University) · Cited by 2

Using Distance Correlation for Efficient Bayesian OptimizationPreprint

Kanazawa, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08993 · Cited by 2

Artificial Intelligence Application to Cognitive Radio Networks

Benmammar, B., & Amraoui, A. (2021) · doi:10.1002/9781119817840.ch9 · Cited by 2

A Simple Approach to Continual Learning by Transferring Skill ParametersPreprint

Zentner, K. R., Julian, R., Puri, U., Zhang, Y., & Sukhatme, G. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10255 · Cited by 2

Deep Reinforcement Learning in Human Activity Recognition: A SurveyPreprint

Nikpour, B., Sinodinos, D., & Armanfard, N. (2022) · doi:10.36227/techrxiv.19172369.v3 · Cited by 2

Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future ProspectsPreprint

Salem, H., Quamar, M. M., Magad, A., Mohammed Elrashidy, Saeed, N., & Masood, M. (2023) · doi:10.36227/techrxiv.23653341 · Cited by 2

MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences

Colucci, A., Juhász, D., Mosbeck, M., Marchisio, A., Rehman, S., Kreutzer, M., Nadbath, G., Jantsch, A., & Shafique, M. (2021) · Design, Automation, and Test in Europe · doi:10.23919/date51398.2021.9474158 · Cited by 1

Tangent Space Least Adaptive Clustering

Buenfil, J., Koelle, S., & Meilă, M. (2021) · International Conference on Machine Learning · Cited by 1

Development of Stock Market Price Application to Predict Purchase and Sales Decisions Using Proximal Policy Optimization Method

Gunawan, A. A. S., Ashifa, S. B., Rumagit, R. Y., & Ngarianto, H. (2021) · doi:10.1109/iccsai53272.2021.9609714 · Cited by 1

QoS based Multi-Agent vs. Single-Agent Deep Reinforcement Learning for V2X Resource Allocation

Bhadauria, S., Ravichandran, L., Roth-Mandutz, E., & Fischer, G. (2021) · IEEE International Symposium on Future Telecommunication Technologies (SOFTT) · doi:10.1109/softt54252.2021.9673150 · Cited by 1

Portfolio management algorithm based on long-term prediction of assets

Lei, M., Pan, X., Gao, S.-S., & Zhang, Y. (2022) · ICBDT · doi:10.1145/3565291.3565315 · Cited by 1

Conditionally Combining Robot Skills using Large Language Models

Zentner, K. R., Julian, R., Ichter, B., & Sukhatme, G. S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611275 · Cited by 2

Optimizing Blockchain Mining Decisions Using Deep Reinforcement Learning Algorithms

Venkatesam, A. S., Reddy, K. S., Hariharan, S., Dash, B. B., Chowdhury, S., & Patra, S. S. (2025) · International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas64557.2025.10967840 · Cited by 1

Architecting Agentic AI Systems: Product and System Design Patterns for Trustworthy Autonomous Decision-Making

Prasad, T. A. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.5064 · Cited by 1

Towards Robust and Safe Decision-Making Under Uncertainty: A Model-Centric View

Bartocci, E., Češka, M., Hudák, D., Novotný, P., & Tappler, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-40121-2_3 · Cited by 1

Machine Learning-Based Open Framework for Multiresolution Multiagent Simulation

Pierzchała, D., & Czuba, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-43890-6_17 · Cited by 1

Spike neuron optimization using deep reinforcement learning

Hui, T. S., & Ishak, M. K. (2021) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v10.i1.pp175-183 · Cited by 1

Generative Adversarial Network Based Status Generation Simulation Approach

Chen, Z., Zhang, Z., Zhao, X. Q., & Guo, L. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-5940-9_19 · Cited by 1

Deep Reinforcement Learning: From First Principles to Reasoning ModelsPreprint

Bista, G. (2026) · arXiv (Cornell University) · doi:10.5281/zenodo.21140609

Deep Reinforcement Learning

(2020) · Springer eBooks · doi:10.1007/978-981-15-4095-0 · Cited by 240

DEEP REINFORCEMENT LEARNING: PRINCIPLES AND APPLICATIONS

Bibi, D. M. (2022) · Computer Science Bulletin · doi:10.71465/csb89

Deep Reinforcement Learning for Adaptive Decision-Making in Real-World Applications

Touati, C., & Kemmar, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7082-8.ch005

Deep Reinforcement Learning, a textbookPreprint

Plaat, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.02135 · Cited by 1