This post is a tutorial on how to set up and run a Double DQN(DDQN) algorithm on ROS based robot TurtleBot 2. By the end, you will have a working environment where you can train the TurtleBot 2 robot using the DDQN algorithm. You will be able to visualize the movements of the robot in a 3D simulation tool, Gazebo.

Visualization of operation of the robot in the 3D simulation tool, Gazebo

This post assumes that you are familiar with ROS and ROS packages, and if this is not the case, I highly recommend going through them before starting off(helpful link: ROS-Creating a workspace). However, with basic knowledge of Linux, python and…


It is crucial to understand why Neural Networks(NN) are predicting what they are predicting, especially if the application is safety-critical. Explainable AI(XAI) refers to approaches that establish a relationship between input and output, that can be easily understood by humans. It helps ensure that NN is using the right features before making predictions.

One of the best aspects of a NN is that it learns the important features from the input data by itself, this lets NNs solve very complex problems but leaves the programmers/designers without a clue about which input features are being given more importance in giving predictions.

Praveenkumar

AI Enthusiast; M.Sc. Electrical Engineering, University of Stuttgart

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