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
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Asymmetric Multiagent Reinforcement Learning
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
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
Docitive networks: an emerging paradigm for dynamic spectrum management
IEEE Wireless Communications
Expertness based cooperative Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Journal of Network and Computer Applications
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We advocate for a novel communication paradigm of docition which facilitates distributed and autonomous networking at minimal control overhead and maximal performance. We consider that the nodes in foreseen networks are intelligent radios able to learn and thus self-adapt to prior set performance targets within a given surrounding environment. We briefly review the state-of-the-art of purely distributed learning algorithms, and we identify the most appropriate approaches allowing for self-adaptation to particular system dynamics. In such distributed settings, however, the learning is typically complex, imprecise and slow due to mutually-impacting decisions resulting in non-stationarities. The docitive paradigm proposes a timely solution which encourages more knowledgeable nodes to teach surrounding nodes to speed up the development of their cognitive state. We advocate for different degrees of docition, such as teaching at start-up or run-time, and demonstrate that this improves the convergence speed and precision of known cognitive algorithms. We evaluate the docitive paradigm in the context of a femtocell network modeled as a multi-agent system, where the agents are the femto access points, implementing a realtime multi-agent reinforcement learning technique known as decentralized Q-learning. We propose different docitive algorithms and we show their superiority to the well know paradigm of independent learning.