Hallucinogenic Deep Reinforcement Learning using Python and Keras

This post is a step by step guide through the paper. We'll cover the technical details and also walk through how you can get a version running on your own machine. Similarly to my post on AlphaZero, I'm not associated with the authors of the paper but just wanted to share my interpretation of their terrific work. We're going to build a reinforcement learning algorithm (an'agent') that gets good at driving a car around a 2D racetrack. At each time-step, the algorithm is fed an observation (a 64 x 64 pixel colour image of the car and immediate surroundings) and needs to return the next set of actions to take -- specifically, the steering direction (-1 to 1), acceleration (0 to 1) and brake (0 to 1). This action is then passed to the environment, which returns the next observation and the cycle starts again.