Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multi-agent radio resource allocation
Mobile Networks and Applications
IEEE Transactions on Mobile Computing
Dynamic Spectrum Access and Management in Cognitive Radio Networks
Dynamic Spectrum Access and Management in Cognitive Radio Networks
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Opportunistic spectrum access with multiple users: learning under competition
INFOCOM'10 Proceedings of the 29th conference on Information communications
COMAS: a cooperative multiagent architecture for spectrum sharing
EURASIP Journal on Wireless Communications and Networking
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Resource allocation is an important issue in cognitive radio systems. It can be done by carrying out negotiation among secondary users. However, significant overhead may be incurred by the negotiation since the negotiation needs to be done frequently due to the rapid change of primary users' activity. In this paper, a channel selection scheme without negotiation is considered for multi-user and multi-channel cognitive radio systems. To avoid collision incurred by non-coordination, each secondary user learns how to select channels according to its experience. Multi-agent reinforcement leaning (MARL) is applied in the framework of Q-learning by considering opponent secondary users as a part of the environment. The dynamics of the Q-learning are illustrated using Metrick-Polak plot. A rigorous proof of the convergence of Q-learning is provided via the similarity between the Q-learning and Robinson-Monro algorithm, as well as the analysis of convergence of the corresponding ordinary differential equation (via Lyapunov function). Examples are illustrated and the performance of learning is evaluated by numerical simulations.