The Deep Planning Network (PlaNet) In the previous article Deep RL: a Model-Based approach (part 2), we saw how Deep Reinforcement Learning (DRL) works and how the model-based approach can improve sample efficiency. In this article, we present a specific algorithm model-based algorithm called PlaNet.

Model-based Deep Reinforcement Learning explained In the previous article Deep RL: a Model-Based approach (part 1), we saw how Deep Reinforcement Learning (DRL) could be very effective and very inefficient. Now we examine how it works and why a model-based approach can drastically improve the sample efficiency. Reinforcement learning In reinforcement learning…

Deep Reinforcement Learning doesn’t really work… Yet Using Deep Reinforcement Learning (RL), we can train an agent to solve a task without explicitly programming it. This approach is so general that, in principle, we can apply it to any sequential decision-making. For example, in 2015, a research team developed a…

Checking the reliability of SHAP methods Our previous article, A Game of Prediction (Part 1), presented four methods, based on the SHAP framework, to explain the neural network result. In this article, we compare and evaluate them using a sanity check.

Coalition Games for explaining DNN Deep neural networks received great attention because it can be proven that under certain assumptions, they are universal approximators. However, how the network reaches a certain prediction is not well understood. Therefore, deep neural networks have been referred to as black-box algorithms. To understand how…

Assessing the scope and quality of results provided by eXplainable AI Sanity Checks In the previous article, we’ve seen a brief introduction of the why’s and the how’s of Explainable Artificial Intelligence. Here we’re are going to see the results of some Sanity Checks experiments. The experiments conducted here aim to answer…

aka Training Self-Driving with Virtual Worlds This series's previous two articles presented some challenges in training self-driving systems and the first methods to overcome: Pixel-level Adaptation. Today we’ll see a second approach: Features-level Adaptation. Feature-level Adaptation This method is based on adding a loss based on the segmentation model’s ability to fool…

aka Training Self-Driving with Virtual Worlds In the previous article, I presented some challenges in training self-driving systems and two methods to overcome them: Pixel-level Adaptation and Features-level Adaptation, both based on Generative Adversarial Network. This article will explain how they work and show our experimental results. Pixel-level Adaptation The first method…

aka Training Self-Driving with Virtual Worlds Self-driving cars should have already been here but have not yet arrived, even if Tesla, with the latest version of the FSD Beta, now seems very close to success. …