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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Multiagent Systems
Introduction to Multiagent Systems
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PHP Developer's Handbook
IP Network-Based Multi-Agent Systems for Industrial Automation: Information Management, Condition Monitoring and Control of Power Systems
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The Journal of Machine Learning Research
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In this paper we show the application of multi-agent modeling and simulation with distributed reinforcement learning to one of the major problems in power system operations, i.e. voltage control. In this research some agents in the power network work together to provide a desirable voltage profile, using a combination of multi-agent system (MAS) technology and some of the reinforcement learning approaches. In this schema, individual agents who are assigned to voltage controller devices in the power system learn from their experiences to control the system voltage, and also cooperate and communicate with each other to satisfy the whole team goals. A detailed evaluation of methods for controlling voltage in power systems, including multi-agent coordination and distributed reinforcement learning (DRL), demonstrates that this framework yields effective plans, good agent coordination, and successful implementation. In the proposed approach, agent development and communication simulation have been carried out in the Java Agent Development (JADE) framework.