Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Open accessAbstract
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have led to multiple successes in solving sequential decision-making problems in various domains, particularly in wireless communications. The next generation of wireless networks is expected to provide scalable, low-latency, ultra-reliable services empowered by the application of data-driven Artificial Intelligence (AI). The key enabling technologies of future wireless networks, such as intelligent meta-surfaces, aerial networks, and AI at the edge, involve more than one agent which motivates the importance of multi-agent learning techniques. Furthermore, cooperation is central to establishing self-organizing, self-sustaining, and decentralized networks. In this context, this tutorial focuses on the role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-enabled wireless networks. The first part of this paper will present a clear overview of the mathematical frameworks for single-agent RL and MARL. The main idea of this work is to motivate the application of RL beyond the model-free perspective which was extensively adopted in recent years. Thus, we provide a selective description of RL algorithms such as Model-Based RL (MBRL) and cooperative MARL and we highlight their potential applications in future wireless networks. Finally, we overview the state-of-the-art of MARL in fields such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAV) networks, and cell-free massive MIMO, and identify promising future research directions. We expect this tutorial to stimulate more research endeavors to build scalable and decentralized systems based on MARL.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent literature identifies notable limitations that constrain AI Agents' scalability in complex, dynamic, multi-step, and/or cooperative scenarios.
“While AI Agents represent a significant leap in artificial intelligence capabilities, particularly in automating narrow tasks through tool-augmented reasoning, recent literature identifies notable limitations that constrain their scalability in complex, dynamic, multi-step, and/or cooperative scenarios [141-143].”
From AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges (2026) · cites this paper as [143]
Section V.A, “Challenges and Implementation Schemes” (page not provided), identifies non-stationarity and an exponentially growing joint action space as challenges for cooperative multi-agent reinforcement learning. This supports the claim about scalability limits in dynamic, cooperative settings, but the paper addresses reinforcement-learning agents in wireless networks, not AI agents generally or tool-augmented task automation; the cited passages do not establish a distinct multi-step scalability limitation. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI Agents have notable limitations that constrain their scalability in complex, dynamic, multi-step, and/or cooperative scenarios.
“While AI Agents represent a significant leap in artificial intelligence capabilities, particularly in automating narrow tasks through tool-augmented reasoning, recent literature identifies notable limitations that constrain their scalability in complex, dynamic, multi-step, and/or cooperative scenarios [143]-[145].”
From AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025) · cites this paper as [145]
The tutorial’s Scope and Section V-A identify non-stationarity, exponentially growing joint action spaces, and partial observability as challenges for multi-agent reinforcement learning, supporting the claim about scalability in cooperative, dynamic settings. It does not establish the same limitations for AI agents generally, including tool-augmented agents; its focus is reinforcement learning for wireless networks. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782 · see citing works
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083 · see citing works
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works
Soft actor-critic Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1801.01290 · Cited by 5,594 · see citing works
(1999) · Cited by 5,147 · see citing works
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems
Saad, W., Bennis, M., & Chen, M. (2020) · IEEE Network · doi:10.1109/mnet.001.1900287 · Cited by 4,985 · see citing works
Trust region policy optimization
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578 · see citing works
Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, S., van Hoof, H., & Meger, D. (2018) · arXiv · doi:10.48550/arxiv.1802.09477 · Cited by 3,650 · see citing works
Markov Games as a Framework for Multi-Agent Reinforcement Learning
Littman, M. L. (1994) · International Conference on Machine Learning · doi:10.1016/b978-1-55860-335-6.50027-1 · Cited by 3,406 · see citing works
Dueling Network Architectures for Deep Reinforcement Learning
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300 · see citing works
Deterministic Policy Gradient Algorithms
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2014) · HAL · Cited by 3,275 · see citing works
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120 · see citing works
Shapley, L. S. (1953) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.39.10.1095 · Cited by 2,425 · see citing works
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D. T., Wang, P., Liang, Y., & Kim, D. I. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916583 · Cited by 2,111 · see citing works
High-Dimensional Continuous Control Using Generalized Advantage Estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1506.02438 · Cited by 2,103 · see citing works
Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents
Tan, M. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50049-6 · Cited by 1,860 · see citing works
6G and Beyond: The Future of Wireless Communications Systems
Akyildiz, I. F., Kak, A., & Nie, S. (2020) · IEEE Access · doi:10.1109/access.2020.3010896 · Cited by 1,419 · see citing works
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Zhang, K., Yang, Z., & Başar, T. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-60990-0_12 · Cited by 1,403 · see citing works
Sutton, R. S. (1990) · Elsevier eBooks · doi:10.1016/b978-1-55860-141-3.50030-4 · Cited by 1,348 · see citing works
Nguyen, T. T., Nguyen, N. D., & Nahavandi, S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2977374 · Cited by 1,306 · see citing works
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
Deisenroth, M. P., & Rasmussen, C. E. (2011) · Cambridge University Engineering Department Publications Database · Cited by 1,136 · see citing works
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningPreprint
Rashid, T., Samvelyan, M., de Witt, C. S., Farquhar, G., Foerster, J., & Whiteson, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.11485 · Cited by 1,019 · see citing works
A Concise Introduction to Decentralized POMDPs
Oliehoek, F. A., & Amato, C. (2016) · SpringerBriefs in Intelligent Systems · doi:10.1007/978-3-319-28929-8 · Cited by 968 · see citing works
Residual Algorithms: Reinforcement Learning with Function Approximation
Baird, L. C. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50013-x · Cited by 948 · see citing works
Cooperative Multi-agent Control Using Deep Reinforcement Learning
Gupta, J. K., Egorov, M., & Kochenderfer, M. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71682-4_5 · Cited by 926 · see citing works
Deep Reinforcement Learning Based Resource Allocation for V2V Communications
Ye, H., Li, G. Y., & Juang, B.-H. F. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2897134 · Cited by 870 · see citing works
Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time
Moore, A., & Atkeson, C. G. (1993) · Machine Learning · doi:10.1023/a:1022635613229 · Cited by 823 · see citing works
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward
Sunehag, P., Lever, G., Gruslys, A., Czarnecki, W. M., Zambaldi, V., Jaderberg, M., Lanctot, M., Sonnerat, N., Leibo, J. Z., Tuyls, K., et al. (2018) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/jsrc7365 · Cited by 820 · see citing works
A survey and critique of multiagent deep reinforcement learning
Hernández-Leal, P., Kartal, B., & Taylor, M. E. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09421-1 · Cited by 747 · see citing works
Shapley, L. S. (1953) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.39.10.1953 · Cited by 678 · see citing works
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2954595 · Cited by 620 · see citing works
Settling the complexity of computing two-player Nash equilibria
Chen, X., Deng, X., & Teng, S. (2009) · Journal of the ACM · doi:10.1145/1516512.1516516 · Cited by 570 · see citing works
Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications
Yang, H., Xiong, Z., Zhao, J., Niyato, D., Xiao, L., & Wu, Q. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3024860 · Cited by 557 · see citing works
Survey of Model-Based Reinforcement Learning: Applications on Robotics
Polydoros, A., & Nalpantidis, L. (2017) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0468-y · Cited by 536 · see citing works
Robust adversarial reinforcement learning
Pinto, L., Davidson, J., Sukthankar, R., & Gupta, A. (2017) · arXiv · doi:10.48550/arxiv.1703.02702 · Cited by 507 · see citing works
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks
Wang, S., Liu, H., Gomes, P. H., & Krishnamachari, B. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2809722 · Cited by 480 · see citing works
Predictive Representations of State
Littman, M. L., & Sutton, R. S. (2001) · Cited by 424 · see citing works
She, C., Sun, C., Gu, Z., Li, Y., Yang, C., Poor, H. V., & Vucetic, B. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3053601 · Cited by 405 · see citing works
Dynamic programming for partially observable stochastic games
Hansen, E. A., Bernstein, D. S., & Zilberstein, S. (2004) · Cited by 401 · see citing works
Planning, Learning and Coordination in Multiagent Decision Processes
Boutilier, C. E. (1996) · Cited by 395 · see citing works
Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications
Qiu, C., Hu, Y., Chen, Y., & Zeng, B. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2921159 · Cited by 384 · see citing works
A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks
Bariah, L., Mohjazi, L., Muhaidat, S., Sofotasios, P. C., Kurt, G. K., Yanıkömeroğlu, H., & Dobre, O. A. (2020) · IEEE Access · doi:10.1109/access.2020.3019590 · Cited by 356 · see citing works
Explainability in deep reinforcement learning
Heuillet, A., Couthouis, F., & Díaz-Rodríguez, N. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106685 · Cited by 337 · see citing works
Shakarami, A., Ghobaei‐Arani, M., & Shahidinejad, A. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107496 · Cited by 333 · see citing works
Qie, H., Shi, D., Shen, T., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2943253 · Cited by 324 · see citing works
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Lei, L., Tan, Y., Zheng, K., Liu, S., Zhang, K., & Shen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988367 · Cited by 307 · see citing works
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
Lin, Y.-C., Hong, Z.-W., Liao, Y.-H., Shih, M.-L., Liu, M.-Y., & Sun, M. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/525 · Cited by 298 · see citing works
Explainable Reinforcement Learning: A Survey
Puiutta, E., & Veith, E. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57321-8_5 · Cited by 278 · see citing works
Multi-Agent Reinforcement Learning:a critical survey
Shoham, Y., Powers, R., & Grenager, T. (2003) · Cited by 267 · see citing works
Rodrigues, T. K., Suto, K., Nishiyama, H., Liu, J., & Kato, N. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2943405 · Cited by 246 · see citing works
MAVEN: Multi-Agent Variational ExplorationPreprint
Mahajan, A., Rashid, T., Samvelyan, M., & Whiteson, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.07483 · Cited by 230 · see citing works
Benchmarking Model-Based Reinforcement Learning
Wang, T., Bao, X., Clavera, I., Hoang, J., Wen, Y., Langlois, E., Zhang, S., Zhang, G., Abbeel, P., & Ba, J. (2019) · arXiv · doi:10.48550/arxiv.1907.02057 · Cited by 226 · see citing works
Chen, Z., & Wang, X. (2020) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-020-01801-6 · Cited by 218 · see citing works
Adversarial Policies: Attacking Deep Reinforcement Learning
Gleave, A., Dennis, M., Wild, C., Kant, N., Levine, S., & Russell, S. (2019) · arXiv · doi:10.48550/arxiv.1905.10615 · Cited by 200 · see citing works
A Survey of Learning in Multiagent Environments: Dealing with Non-StationarityPreprint
Hernández-Leal, P., Kaisers, M., Baarslag, T., & de Cote, E. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.09183 · Cited by 187 · see citing works
Cao, Z., Zhou, P., Li, R., Huang, S., & Wu, D. O. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2968951 · Cited by 184 · see citing works
On the Pitfalls of Measuring Emergent Communication
Lowe, R., Foerster, J. N., Boureau, Y.-L., Pineau, J., & Dauphin, Y. (2019) · Adaptive Agents and Multi-Agent Systems · doi:10.65109/knvj7743 · Cited by 170 · see citing works
6G White Paper on Machine Learning in Wireless Communication NetworksPreprint
Ali, S., Saad, W., Rajatheva, N., Chang, K., Steinbach, D., Sliwa, B., Wietfeld, C., Mei, K., Shiri, H., Zepernick, H., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13875 · Cited by 161 · see citing works
Learning Efficient Multi-agent Communication: An Information Bottleneck ApproachPreprint
Wang, R., He, X., Yu, R.-S., Qiu, W., An, B., & Rabinovich, Z. (2019) · International Conference on Machine Learning · doi:10.48550/arxiv.1911.06992 · Cited by 160 · see citing works
SBEED: Convergent Reinforcement Learning with Nonlinear Function ApproximationPreprint
Dai, B., Shaw, A., Li, L. C., Xiao, L., He, N., Liu, Z., Chen, J., & Song, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.10285 · Cited by 158 · see citing works
Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues
Zhu, H., Cao, Y., Wang, W., Jiang, T., & Jin, S. (2018) · IEEE Network · doi:10.1109/mnet.2018.1800109 · Cited by 153 · cited 1× · see citing works
“Other surveys focus on specific applications of DRL such as autonomous Internet-of-Things [11], resource management for 5G heterogeneous networks [12], and mobile edge caching [13].”
BackgroundMulti-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications
Li, Z., & Guo, C. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2961405 · Cited by 147 · see citing works
Multi-agent reinforcement learning for resource allocation in IoT networks with edge computing
Liu, X., Yu, J., Feng, Z., & Gao, Y. (2020) · China Communications · doi:10.23919/jcc.2020.09.017 · Cited by 133 · see citing works
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal PolicyPreprint
Liu, B., Cai, Q., Yang, Z., & Wang, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10306 · Cited by 133 · see citing works
Networked Multi-Agent Reinforcement Learning in Continuous Spaces
Zhang, K., Yang, Z., & Başar, T. (2018) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2018.8619581 · Cited by 128 · see citing works
Al-Eryani, Y. F., Akrout, M., & Hossain, E. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3018825 · Cited by 120 · see citing works
Self-citationNeural Policy Gradient Methods: Global Optimality and Rates of ConvergencePreprint
Wang, L., Cai, Q., Yang, Z., & Wang, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01150 · Cited by 119 · see citing works
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and AlgorithmsPreprint
Zhang, K., Yang, Z., & Başar, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10635 · Cited by 117 · see citing works
Optimization for Reinforcement Learning: From a single agent to cooperative agents
Lee, D., He, N., Kamalaruban, P., & Cevher, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2976000 · Cited by 113 · see citing works
Deep Reinforcement Learning and the Deadly TriadPreprint
van Hasselt, H. P., Doron, Y., Strub, F., Hessel, M., Sonnerat, N., & Modayil, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02648 · Cited by 101 · see citing works
When to use parametric models in reinforcement learning?Preprint
van Hasselt, H. P., Hessel, M., & Aslanides, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05243 · Cited by 101 · see citing works
Deep Learning at the Mobile Edge: Opportunities for 5G Networks
McClellan, M., Cervelló-Pastor, C., & Sallent, S. (2020) · Applied Sciences · doi:10.3390/app10144735 · Cited by 98 · see citing works
Tactics of Adversarial Attack on Deep Reinforcement Learning AgentsPreprint
Lin, Y.-C., Hong, Z.-W., Liao, Y.-H., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06748 · Cited by 91 · see citing works
Deep Reinforcement Learning for Distributed Dynamic MISO Downlink-Beamforming Coordination
Ge, J., Liang, Y., Joung, J., et al. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3004524 · Cited by 84 · see citing works
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks
Sana, M., Domenico, A. D., Yu, W., et al. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3003719 · Cited by 80 · see citing works
Cooperative and Distributed Reinforcement Learning of Drones for Field CoveragePreprint
Pham, H. X., La, H. M., Feil-Seifer, D., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.07250 · Cited by 69 · see citing works
Zia, K., Javed, N. M., Sial, M. N., et al. (2019) · IEEE Access · doi:10.1109/access.2018.2890210 · Cited by 69 · see citing works
Survey on Reinforcement Learning Applications in Communication Networks
Qian, Y., Wu, J. H., Wang, R., et al. (2019) · Journal of Communications and Information Networks · doi:10.23919/jcin.2019.8917870 · Cited by 69 · see citing works
Cooperative Edge Caching: A Multi-Agent Deep Learning Based Approach
Zhang, Y., Feng, B., Quan, W., Tian, A., Sood, K., Lin, Y., & Zhang, H. (2020) · IEEE Access · doi:10.1109/access.2020.3010329 · Cited by 67 · see citing works
Artificial Intelligence for Wireless Caching: Schemes, Performance, and Challenges
Sheraz, M., Ahmed, M., Hou, X., Li, Y., Jin, D., Han, Z., & Jiang, T. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3008362 · Cited by 63 · see citing works
Convergent Policy Optimization for Safe Reinforcement LearningPreprint
Yu, M., Yang, Z., Kolar, M., & Wang, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12156 · Cited by 62 · see citing works
Wang, R., Yadav, A., Makled, E. A., Dobre, O. A., Zhao, R., & Varshney, P. K. (2019) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2019.2948992 · Cited by 58 · see citing works
A Survey on Multi-Agent Reinforcement Learning Methods for Vehicular Networks
Althamary, I., Huang, C., & Lin, P. (2019) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2019.8766739 · Cited by 57 · see citing works
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks
Zhong, C., Gursoy, M. C., & Velipasalar, S. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8762084 · Cited by 51 · see citing works
Secure Beamforming for Multiple Intelligent Reflecting Surfaces Aided mmWave Systems
Xiu, Y., Zhao, J., Yuen, C., et al. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.3028135 · Cited by 48 · see citing works
Xie, R., Tang, Q., Liang, C., Yu, F. R., & Huang, T. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3004223 · Cited by 43 · see citing works
Dynamic Task Offloading in Multi-Agent Mobile Edge Computing Networks
Heydari, J., Ganapathy, V., & Shah, M. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013115 · Cited by 41 · see citing works
Composable Planning with AttributesPreprint
Zhang, A., Lerer, A. K., Sukhbaatar, S., Fergus, R., & Szlam, A. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.00512 · Cited by 38 · see citing works
A Machine Learning Approach to RoutingPreprint
Valadarsky, A., Schapira, M., Shahaf, D., & Tamar, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.03074 · Cited by 37 · see citing works
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal PoliciesPreprint
Zhang, K., Koppel, A., Zhu, H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08383 · Cited by 37 · see citing works
Lee, Y. L., & Qin, D. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023331 · Cited by 36 · see citing works
Coordinated Passive Beamforming for Distributed Intelligent Reflecting Surfaces Network
He, J., Yu, K., & Shi, Y. (2020) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2020-spring48590.2020.9129476 · Cited by 35 · see citing works
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
Wang, B., & Hegde, N. (2019) · neural information processing systems · Cited by 34 · see citing works
Tactics of Adversarial Attack on Deep Reinforcement Learning AgentsPreprint
Lin, Y.-C., Hong, Z.-W., Liao, Y.-H., Shih, M.-L., Liu, M.-Y., & Sun, M. (2017) · arXiv (Cornell University) · Cited by 33 · see citing works
A Finite Time Analysis of Two Time-Scale Actor Critic MethodsPreprint
Wu, Y., Zhang, W., Xu, P., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.01350 · Cited by 33 · see citing works
Improving Sample Complexity Bounds for (Natural) Actor-Critic AlgorithmsPreprint
Xu, T., Wang, Z., & Liang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12956 · Cited by 33 · see citing works
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell NetworksPreprint
Ahmed, K. I., & Hossain, E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.13032 · Cited by 31 · see citing works
Self-citationShen, Z., Ribeiro, A., Hassani, H., et al. (2019) · International Conference on Machine Learning · Cited by 31 · see citing works
The Application of Multi-Agent Reinforcement Learning in UAV Networks
Cui, J., Liu, Y., & Nallanathan, A. (2019) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2019.8756984 · Cited by 30 · see citing works
Non-asymptotic Convergence Analysis of Two Time-scale (Natural) Actor-Critic AlgorithmsPreprint
Xu, T., Wang, Z., & Liang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03557 · Cited by 29 · see citing works
She, C., Sun, C., Gu, Z., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06010 · Cited by 26 · see citing works
Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks
Barazideh, R., Semiari, O., Niknam, S., et al. (2020) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops49005.2020.9145080 · Cited by 23 · see citing works
Gündüz, D. (2021) · IRIS UNIMORE (University of Modena and Reggio Emilia) · doi:10.1109/tsusc.2020.3025139 · Cited by 23 · see citing works
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement LearningPreprint
Pan, X., Wang, W., Zhang, X., Li, B., Yi, J., & Song, D. X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.11082 · Cited by 19 · see citing works
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent AdvancesPreprint
Zhang, K., Yang, Z., & Başar, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03821 · Cited by 18 · see citing works
Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach
Naderializadeh, N., & Hashemi, M. (2019) · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/ieeeconf44664.2019.9049050 · Cited by 17 · see citing works
Keeping in Touch with Collaborative UAVs: A Deep Reinforcement Learning Approach
Yang, B., & Liu, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/78 · Cited by 15 · see citing works
Improving Sample Complexity Bounds for Actor-Critic AlgorithmsPreprint
Xu, T., Wang, Z., & Liang, Y. (2020) · arXiv (Cornell University) · Cited by 13 · see citing works
Stochastic Variance-Reduced Policy GradientPreprint
Papini, M., Binaghi, D., Canonaco, G., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05618 · Cited by 13 · see citing works
Multi-Agent Reinforcement Learning with Multi-Step Generative ModelsPreprint
Krupnik, O., Mordatch, I., & Tamar, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10251 · Cited by 10 · see citing works
Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet RoutingPreprint
Mao, H., Gong, Z., Zhang, Z., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05561 · Cited by 10 · see citing works
Yang, H., Xiong, Z., Zhao, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12539 · Cited by 10 · see citing works
Application of Deep Reinforcement Learning to UAV Fleet Control
Tožička, J., Szulyovszky, B., de Chambrier, G., et al. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-01057-7_85 · Cited by 9 · see citing works
Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning
Naghizadeh, P., Gorlatova, M. A., Lan, A., et al. (2019) · doi:10.1109/infocom.2019.8737652 · Cited by 7 · see citing works
Coordinated Passive Beamforming for Distributed Intelligent Reflecting Surfaces NetworkPreprint
He, J., Yu, K., & Shi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05915 · Cited by 6 · see citing works
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized SweepingPreprint
Bargiacchi, E., Verstraeten, T., Roijers, D. M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.07527 · Cited by 3 · see citing works
Ultra-dense low data rate (UDLD) communication in the THz
Singh, R., & Sicker, D. (2020) · ACM International Conference on Nanoscale Computing and Communication (NANOCOM) · doi:10.1145/3411295.3411317 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 13
- [34]34. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, "Asynchronous methods for deep rein- forcement learning," in International conference on machine learning, pp. 1928-1937, 2016.
- [38]38. C. J. Watkins and P. Dayan, "Q-learning," Machine learning, vol. 8, no. 3-4, pp. 279-292, 1992.
- [50]50. B. Dai, N. He, Y. Pan, B. Boots, and L. Song, "Learning from conditional distributions via dual embeddings," in Artificial Intelligence and Statistics, pp. 1458-1467, 2017.
- [55]55. P. Xu, F. Gao, and Q. Gu, "An improved convergence analysis of stochastic variance-reduced policy gradient," in Uncertainty in Artificial Intelligence, pp. 541-551, PMLR, 2020.
- [65]65. A. Zhang, S. Sukhbaatar, A. Lerer, A. Szlam, and R. Fergus, "Compos- able planning with attributes," in International Conference on Machine Learning, pp. 5842-5851, 2018.
- [70]70. I. Mordatch and J. Hamrick, "Tutorial on model-based methods in reinforcement learning," ICML, 2020.
- [71]71. M. Janner, J. Fu, M. Zhang, and S. Levine, "When to trust your model: Model-based policy optimization," in Advances in Neural Information Processing Systems, pp. 12519-12530, 2019.
- [72]72. Y. Gal, R. McAllister, and C. E. Rasmussen, "Improving PILCO with bayesian neural network dynamics models," in Data-Efficient Machine Learning workshop, ICML, vol. 4, p. 34, 2016.
- [73]73. K. Chua, R. Calandra, R. McAllister, and S. Levine, "Deep rein- forcement learning in a handful of trials using probabilistic dynamics models," in Advances in Neural Information Processing Systems, pp. 4754-4765, 2018.
- [77]77. X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, "Infogan: Interpretable representation learning by informa- tion maximizing generative adversarial nets," in Advances in neural information processing systems, pp. 2172-2180, 2016.
- [84]84. J. Jiang and Z. Lu, "Learning attentional communication for multi- agent cooperation," in Advances in neural information processing systems, pp. 7254-7264, 2018.
- [88]88. R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. P. Abbeel, and I. Mor- datch, "Multi-agent actor-critic for mixed cooperative-competitive en- vironments," in Advances in neural information processing systems, pp. 6379-6390, 2017.
- [91]91. J. Castellini, F. A. Oliehoek, R. Savani, and S. Whiteson, "The representational capacity of action-value networks for multi-agent reinforcement learning," arXiv preprint arXiv:1902.07497, 2019. · arXiv
Cited by
Sorted by citations
AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103599 · Cited by 244 · see citing works
“…represent a significant leap in artificial intelligence capabilities, particularly in automating narrow tasks through tool-augmented reasoning, recent literature identifies notable limitations that constrain their scalability in complex, dynamic, multi-step, and/or cooperative scenarios [141–143].”
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025) · SuperIntelligence - Robotics - Safety & Alignment · doi:10.70777/si.v2i3.15161 · Cited by 186 · see citing works
A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41 · see citing works
Lou, X., Zhou, Q., Dong, Z., Bai, L., Su, J., & Yue, H. (2025) · Journal of Orthopaedic Translation · doi:10.1016/j.jot.2025.07.010 · Cited by 17 · see citing works
- Authors
- Feriani, A., & Hossain, E.
- Affiliations
- University of Manitoba (CA)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2021
- Volume
- 23
- Issue
- 2
- Pages
- 1226-1252
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3101189765
- Cited by
- 410
- References
- 123
- Citations by year
- 2026: 58 · 2025: 99 · 2024: 91 · 2023: 82 · 2022: 63 · 2021: 17
- FWCI
- 22.16
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Discovery Grant from the Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Keywords
- deep reinforcement learning, multi-agent reinforcement learning, model-based reinforcement learning, cooperative multi-agent learning, mobile edge computing, UAV networks, cell-free massive MIMO, intelligent metasurfaces, aerial networks, decentralized networks, sequential decision making
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
