A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems

This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method

Architectures for Cognitive Radio Testbeds and Demonstrators – An Overview

Wireless communication standards are developed at an ever-increasing rate of pace, and significant amounts of effort is put into research for new communication methods and concepts. On the physical layer, such topics include MIMO, cooperative communication, and error control coding, whereas research on the medium access layer includes link control, network topology, and cognitive radio. At the same time, implementations are moving from traditional fixed hardware architectures towards software, allowing more efficient development. Today, field-programmable gate arrays (FPGAs) and regular desktop computers are fast enough to handle complete baseband processing chains, and there are several platforms, both open-source and commercial, providing such solutions. The aims of this paper is to give an overview of five of the available platforms and their characteristics, and compare the features and performance measures of the different systems