Search Results for "code coverage"

All RL Algorithms from Scratch is an educational reinforcement learning repository built around readable Python and Jupyter Notebook implementations. Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and...

PythonRobotics is a Python code collection and textbook for learning robotics algorithms through readable examples. It covers practical topics such as localization, mapping, path planning, path tracking, control, SLAM, and autonomous navigation. The project is written to make each algorithm’s core idea easy to understand, rather than hiding the logic behind large frameworks. It keeps dependencies minimal so learners can focus on the math, implementation, and behavior of each robotics method....

Dwifft is a small Swift library that tells you what the "diff" is between two collections, namely, the series of "edit operations" required to turn one into the other. It also comes with UIKit bindings, to automatically, animatedly keep a UITableView/UICollectionView in sync with a piece of data by making the necessary row/section insertion/deletion calls for you as the data changes. Dwifft is a Swift library that does two things. The first thing sounds interesting but perhaps only...

The React Fiber Architecture project is a detailed technical document that explains the internal design and behavior of React Fiber, the core algorithm that powers modern React rendering. Rather than being a traditional code library, it serves as an educational deep dive into how React manages updates, scheduling, and reconciliation under the hood. The document explores how Fiber replaces the older stack-based reconciliation algorithm with a more flexible system that breaks rendering work...