Deep Reinforcement Learning Based Home Energy Management System (HEMS)
International audience
Optimal maintenance of deteriorating systems integrating deep reinforcement learning and Bayesian inference
An issue of utmost significance constitutes the maintenance of engineering systems exposed to corrosive environments, e.g. coastal and marine environments, highly acidic environments, etc. The most beneficial sequence of maintenance decisions, i.e. the one that corresponds to the minimum maintenance cost, can be sought as the solution to an optimiz...
Photovoltaic self-consumption optimization for Home Microgrid: A Deep Reinforcement Learning approach
Increasing penetration of renewable energy sources (PV, Wind) due to environmental constraints, impose several technical challenges to power system operation. The fluctuating and intermittent nature of wind and solar energy requires constant supply-demand balance for electric grid stability purposes.Self-consumption is a regulatory framework intend...
Improving a Reinforcement Learning Negotiating Agent’s Performance by Extracting Information from the Opponent’s Sequenc...
With the prospects of decentralized multi-agent systems becoming more prevalent in daily life, automated negotiation agents have made their place in these collaborative settings. They are an approach to promote communication between the agents in reaching solutions that are better for all involved.
Recent literature has shown great potential in usin...
Neural Style Transfer with Twin-Delayed DDPG for Shared Control of Robotic Manipulators
Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defined as a combination of Content and Style: the Content can be conceptually defined as the "what" and the Style as the "how" of said element. In this context, we propose a ...
Smart home energy management systems : Optimization, rules and regulations, and learning / Intelligente Energiemanagemen...
Buildings are one of the main drivers of global energy consumption and CO2 emissions. Efficient home energy management systems and residential sector coupling play a key role in supporting the energy transition in this sector. At the same time, the European Commission calls for more small-scale renewable energy producers to actively participate in th...
Preparing for the next COVID: Deep Reinforcement Learning trained Artificial Intelligence discovery of multi-modal immun...
Published in bioRxiv
Background: Despite a great deal of interest in the application of artificial intelligence (AI) to sepsis/critical illness, most current approaches are limited in their potential impact: prediction models do not (and cannot) address the lack of effective therapeutics and current approaches to enhancing the treatment of sepsis focus on optimizing th...
Automatic Curriculum Learning for Developmental Machine Learners
A long-standing goal of Machine Learning (ML) and AI at large is to design autonomous agents able to efficiently interact with our world. Towards this, taking inspirations from the interactive nature of human and animal learning, several lines of works focused on building decision making agents embodied in real or virtual environments. In less than...
Multi-Agent Deep Reinforcement Learning for Wireless-Powered UAV Networks
Unmanned Aerial Vehicles (UAVs) have attracted much attention lately and are being used in a multitude of applications. But the duration of being in the sky remains to be an issue due to their energy limitation. In particular, this represents a major challenge when UAVs are used as base stations (BSs) to complement the wireless network. Therefore, ...
Deep Reinforcement Learning for Optimal Energy Management of Multi-energy Smart Grids
This paper proposes a Deep Reinforcement Learning approach for optimally managing multi-energy systems in smart grids. The optimal control problem of the production and storage units within the smart grid is formulated as a Partially Observable Markov Decision Process (POMDP), and is solved using an actor-critic Deep Reinforcement Learning algorith...
