DIET Controller: Dynamic Indoor Environment using Deep Reinforcement Learning
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
The energy services delivered to urban areas in Switzerland make up more than 45% of the national energy consumption. Thus, in the framework of the Tetraener European project, a methodology has been developed to design low temperature thermal networks base ...
The efficient thermal control of buildings is a complex problem. Non controlled perturbations like user behaviour and meteorological conditions can change much with time and make this task difficult. This work proposes considering the building as a 'living ...
The goal of the project DELTA was to develop an optimal blind controller for a room or a building, taking into account the following factors: - optimisation of thermal comfort - optimisation of daylighting - minimisation of energy consumption - priority gi ...
Following the pioneering work of the AGS Tokyo half project, a geographical information system has been developed to model the energy requirement of a urban area. The purpose of this platform is to model with sufficient details the energy services requirem ...
From a sustainable development perspective, the newly developed automatic controllers for building services are very promising in that they increase energy efficiency and reduce commissioning and maintenance costs. But a major problem has appeared as the a ...
Stress and genetic background regulate different aspects of behavioral learning through the action of stress hormones and neuromodulators. In reinforcement learning (RL) models, meta-parameters such as learning rate, future reward discount factor, and expl ...