Design and implementation of an environment for Learning to Run a Power Network (L2RPN)
This report summarizes work performed as part of an internship at INRIA, in partial requirement for the completion of a master degree in math and informatics. The goal of the internship was to develop a software environment to simulate electricity transmission in a power grid and actions performed by operators to maintain this grid in security. Our environment lends itself to automate the control of the power grid with reinforcement learning agents, assisting human operators. It is amenable to organizing benchmarks, including a challenge in machine learning planned by INRIA and RTE for 2019. Our framework, built on top of open-source libraries, is available at https://github.com/MarvinLer/pypownet. In this report we present intermediary results and its usage in the context of a reinforcement learning game.
Code (1)
Tasks
Mathreinforcement-learningReinforcement LearningReinforcement Learning (RL)RTESimilar Papers 제목 키워드 기반
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical applications is expanding, emerging DNNs experience massive growth in terms of computation power. It raises the necessity of improving the …
High-Level SynthesisProject CLAI: Instrumenting the Command Line as a New Environment for AI Agents
This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI). The CLAI platform sets up the CLI as a new environment for AI researchers to conquer by …
AI AgentControlling the Wireless Power Transfer Mechanism of the Both-Sides Retrodirective System
To achieve efficient long-range wireless power transfer (WPT), large antenna systems are necessary spanning lengths of tens to thousands of meters in one dimension. This creates an array in the order of at least hundreds…
Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control
This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environme…
Reconfigurable Digital Channelizer Design Using Factored Markov Decision Processes
In this work, a novel digital channelizer design is developed through the use of a compact, system-level modeling approach. The model efficiently captures key properties of a digital channelizer system and its time-varyi…