UROP Project

Deep Reinforcement Learning for Autonomous Machinery

Contact

Name

Daniel Holder

Program Director UROP

Telephone

workPhone
+49 241 80-90695

E-Mail

Key Info

Basic Information

Project Offer-Number:
1143
Category:
RWTH UROP, UROP Network, UROP International
Field:
Computer Science
Faculty:
5
Organisation unit:
RWTH Aachen University, Advanced Mining Technology (AMT)
Language Skills:
English or German

MoveOn

The interest in Reinforcement Learning (RL) has recently surged. RL is the computational approach to learning from interaction. A RL agent is mapping situations it encounters within a given environment to actions that lead to the most reward. The agent does not always know the environment beforehand and therefore has to discover what actions yield the most reward. We at the Advanced Mining Technology (AMT) Institute are committed to providing technologies and system solutions for harsh environments. Longwall Mining is one such harsh environment, in which a longwall machine uses height-adjustable, rotating cutting drums to extract mineral resources. Aim of this project is to develop a reinforcement learning model that can steer a longwall machine and adjust its cutting drum height so that mineral resource extraction is maximized and material wear and tear is minimized. As resource efficiency and social responsibility are our misson, this project brings our core values to life.

Task

- Set-Up and refine a simulation environment - Choose between different algorithms/models - Implementing reinforcement learning models - Evaluating test results

Requirements

- Intermediate Python Skills - Research oriented mindset - String problem solving skills