Video – Philipp Waeltermann

By - Philipp Waeltermann
Posted on October 31, 2022November 1, 2022
Posted in Home, Showcase

Deep Reinforcement Learning in Lunar Lander Environment

This was one of the most fun projects I have worked on. The task was to train a Reinforcement Learning agent to land the spacecraft in the lunar lander environment. In order to do this, the agent will have to learn to control the three boosters of a spacecraft based on the spacecraft’s position, velocity,

By - Philipp Waeltermann
Posted on August 13, 2017November 1, 2022
Posted in Home, Showcase, Work & Education

Internship at dSPACE

During my internship at dSPACE I developed a Lane Centering and lane change decision making algorithm using dSPACE’s SIL tools. The controllers were developed in Simulink and tested with dSPACE MotionDesk, dSPACE VEOS, dSPACE ModelDesk, and dSPACE ControlDesk. The lane centering controller was based on an the Stanley Controller used by Stanford during the DARPA