Secondary Voltage Collaborative Control of Distributed Energy System via Multi-Agent Reinforcement Learning

An Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources

This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO...

Application of Reinforcement Learning to Control a Multi-agent System

This study takes place in the context of multi-agent systems (MAS), and especially reactive ones. In such a system, interactions are essential, and trigger a collective behaviour that is not directly linked to the individual ones. Whereas the evolution of the system is unknown if not tried, the regularity of emergent structures in the system is observable and forms a global behaviour. In this p...

Multi-Agent System Based Distributed Voltage Control in Distribution Systems

Crowd Simulation Via Multi-Agent Reinforcement Learning

Artificial intelligence is frequently used to control virtual characters in movies and games. When these characters appear in crowds, controlling them is called crowd simulation. In this paper, I suggest that crowd simulation could be accomplished by multi-agent reinforcement learning, a method by which groups of agents can learn to act autonomously in their environment. I present a case study ...

$QD$-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations

The paper considers a class of multi-agent Markov decision processes (MDPs), in which the network agents respond differently (as manifested by the instantaneous one-stage random costs) to a global controlled state and the control actions of a remote controller. The paper investigates a distributed reinforcement learning setup with no prior information on the global state transition and local ag...