Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Attack abstraction using a multiagent system for intrusion detection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Soft computing based multi-agent marketing decision support system
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Automatica (Journal of IFAC)
Expertness based cooperative Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Study on Expertise of Agents and Its Effects on Cooperative -Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Multi-Agent systems have proved powerful in various sciences and engineering problems. This paper proposes a novel Multi-Agent Active Noise Control ANC formulation via the credit assignment approach. The introduced ANC removes multi-tonal acoustic noises in the environment invoking reinforcement learning techniques. In some multi-agent systems, for the training of all agents, only one reward is available. It is clear that this reward does not belong to one particular agent. The assignment of this reward to the agents is a problem which is known as Multi-Agent Credit Assignment MCA. In this research, each agent is responsible for reducing the noise power of one single harmonic, while only the total noise power of the signals is known. Therefore, it is required to assign a power contribution to each single harmonic. To resolve this problem, at first, the Knowledge Evaluation Based Critic Assignment KEBCA idea with proper modification is used and then a new method is introduced for this special problem. Simulation results show good improvement in the system performance by switching the single agent into the multi-agent system.