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carnd-advanced-lane-lines

Implementation of an advanced lane finding algorithm. Approach includes perspective transformation to calculate the curvature of the detected lanes.

carnd-behavioral-cloning-p3

An important milestone of the "Self Driving Car Engineer" Certificate from Udacity. I was using methods of machine learning to copy my own driving skills in a simulator to train a convolutional neural network (CNN). After training the CNN was able to drive the simulated car autonomously around a race track.

carnd-camera-calibration

Images and notebook for camera calibration

carnd-capstone

Final Project of the Udacity Self-Driving Car Nanodegree: Implementation of a Perception, Planning and Control Module with ROS.

carnd-kidnapped-vehicle-project

The purpose of the project is to implement a particle filter able to localize a vehicle position and yaw within the values specified in a set of given parameters.

carnd-lanelines-p1

Classic approach to find lane lines. The pipeline finds lane lines on the road under changing light conditions and different lane mark colors.

carnd-path-planning-project

Create a path planner that is able to navigate a car safely around a virtual highway

carnd-pid-control-project

Implementation of a PID Controller that is able to drive a vehicle around a simulated race track.

carnd-traffic-sign-classifier-project

Traffic Sign Recognition with a convolutional neural network(CNN).

deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

deeprl-p2-continuous-control

Project 2 of Udacity's Deep Reinforcement Learning nanodegree program

deeprl-p2-continuouscontrol

Implementation of DDPG algorithm with PyTorch to control a set of robotic arms

deeprl-p3-collaboration-competition

Project 3 of Udacity's Deep Reinforcement Learning nanodegree program.

openai-taxi-v2

Udacity Reinforcement Learning nano Degree Lab

rl-nd-taxigym

Implementing an Reinforcement Learning Algorithm for OpenAIs Taxi Gym

rl-nd_p1_navigation

rl-nd_p2_continous-control

rl-nd_p3-collaboration-competition