🌈Rainbow Brackets / Rainbow Parentheses for IntelliJ based IDEs/Android Studio

Modularized Implementation of Deep RL Algorithms in PyTorch

🌈 React Rainbow Components. Build your web application in a snap.

Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

🌈一个在你编程时持续夸你写的牛逼的扩展，可以根据代码关键字播放贴近代码意义的真人语音。Inspired by vscode-rainbow-fart

🌈‒ the Ethereum wallet that lives in your pocket

🌈 A fluent way to apply gradations and tinting for Android.

A PyTorch implementation of Rainbow DQN agent

An Arduino based music visualizer using the FastLED library and a strip of individually addressable LEDs

Because everybody loves rainbows.

Golang Helper for beautiful CLI Applications

PHP API which allows you to search players and get their R6 stats

一个在你编程时疯狂称赞你的 IDEA扩展插件,兼容VSCode版本语音包 | An IDEA extension that keeps giving you compliment while you are coding, it will checks…

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Simple hack to display the colors of the rainbow flag in the GitHub language bar. #lovewins

Rainbow is a scriptable, cross-platform, 2D game engine.

Sitecore Courier aims to fill the gap between the development and production environments when building websites with Sitecore CMS.

🌈 Hide sticky headers and color the menu bar on Safari for Yosemite

A Livesplit-inspired speedrunning split timer for Linux/macOS terminal. Supports global hotkeys.

TD3, SAC, IQN, Rainbow, PPO, Ape-X, NoisyNets, PER, etc

High-quality implementations of deep reinforcement learning algorithms for experiments

Make your bash prompt dynamically and randomly rainbow

AKA the connected catapult!

Deep reinforcement learning baselines base on OpenAI. More algorithms are included, such as Rainbow: Combining Improvements in Deep Reinforcement Learning

r6operators is a collection of high-quality vectorized Rainbow Six: Siege Operator icons & metadata for Node.js

Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"

An advanced serialization and comparison system for Sitecore. Designed for easy merging with YAML-based serialization, and a generic merge/deserialization framework. Works well with Unicorn, or by itself.