Technical Note: \cal Q-Learning
Machine Learning
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Cognitive radio for flexible mobile multimedia communications
Mobile Networks and Applications - Special issue on Mobile Multimedia Communications (MOMUC '99)
Multiagent learning using a variable learning rate
Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Impact of interference on multi-hop wireless network performance
Proceedings of the 9th annual international conference on Mobile computing and networking
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Best-Response Multiagent Learning in Non-Stationary Environments
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
MOAR: A Multi-Channel Opportunistic Auto-Rate Media Access Protocol for Ad Hoc Networks
BROADNETS '04 Proceedings of the First International Conference on Broadband Networks
Utilization and fairness in spectrum assignment for opportunistic spectrum access
Mobile Networks and Applications
Auction-based spectrum sharing
Mobile Networks and Applications
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Jamming-resistant Key Establishment using Uncoordinated Frequency Hopping
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
CRAHNs: Cognitive radio ad hoc networks
Ad Hoc Networks
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Spectrum management of cognitive radio using multi-agent reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Industry track
JENNA: a jamming evasive network-coding neighbor-discovery algorithm for cognitive radio networks
IEEE Wireless Communications
Nash convergence of gradient dynamics in general-sum games
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks
IEEE Transactions on Wireless Communications
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Value-function reinforcement learning in Markov games
Cognitive Systems Research
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Reinforcement Learning: An Introduction
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
This paper introduces a novel multi-agent multi-state reinforcement learning exploration scheme for dynamic spectrum access and dynamic spectrum sharing in wireless communications. With the multi-agent multi-state reinforcement learning, cognitive radios can decide the best channels to use in order to maximize spectral efficiency in a distributed way. However, we argue that the performance of spectrum management, including both dynamic spectrum access and dynamic spectrum sharing, will largely depend on different reinforcement learning exploration schemes, and we believe that the traditional multi-agent multi-state reinforcement learning exploration schemes may not be adequate in the context of spectrum management. We then propose a novel reinforcement learning exploration scheme and show that we can improve the performance of multi-agent multi-state reinforcement learning based spectrum management by using the proposed reinforcement learning exploration scheme. We also investigate various real-world scenarios, and confirm the validity of the proposed method.