Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems
نویسندگان
چکیده
منابع مشابه
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...
متن کاملDecentralized multi-agent reinforcement learning in average-reward dynamic DCOPs
Researchers have introduced the Dynamic Distributed Constraint Optimization Problem (Dynamic DCOP) formulation to model dynamically changing multi-agent coordination problems, where a dynamic DCOP is a sequence of (static canonical) DCOPs, each partially different from the DCOP preceding it. Existing work typically assumes that the problem in each time step is decoupled from the problems in oth...
متن کاملReinforcement Learning Based PID Control of Wind Energy Conversion Systems
In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...
متن کاملA Self-organizing Multi-agent System for Adaptive Continuous Unsupervised Learning in Complex Uncertain Environments
Introduction. Continuous learning and online decisionmaking in complex dynamic environments under conditions of uncertainty and limited computational recourses represent one of the most challenging problems for developing robust intelligent systems. The existing task of unsupervised clustering in statistical learning requires the maximizing (or minimizing) of a certain similarity-based objectiv...
متن کامل1203 Multi - Policy Optimization in Decentralized Autonomic Systems ( Extended
This paper addresses the challenge of multi-policy optimization in decentralized autonomic systems. We evaluate several multi-policy reinforcement learning-based optimization techniques in an urban traffic control simulation, a canonical example of a decentralized autonomic system. Our results indicate that W-learning, which learns separately for each policy and then selects between nominated a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008