Know an awesome toolkit in reinforcement learning? Tell us through the chat button on bottom right!

BYU Holodeck

Brigham Young University

Holodeck is a simulator based on the Unreal Engine that can be used for research, classes, or fun. It consists of agents (including humanoid robots and UAVs), environments (such as cities, forests, and deserts), Linux and Windows support, and a set of python bindings that make programmatic interaction easy. Plus, it can be run headlessly and/or containerized for massively parallel deep reinforcement learning research.

DeepMind Control Suite and Package

DeepMind

The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are written in Python and powered by the MuJoCo physics engine, making them easy to use and modify.

DeepMind Lab

DeepMind

DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. DeepMind Lab provides a suite of challenging 3D navigation and puzzle-solving tasks for learning agents. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning.

General Video Game AI Competition

The GVG-AI Competition explores the problem of creating controllers for general video game playing. How would you create a single agent that is able to play any game it is given? Could you program an agent that is able to play a wide variety of games, without knowing which games are to be played? Can you create an automatic level generation that designs levels for any game is given?

Gibson

Stanford

You shouldn't play video games all day, so shouldn't your AI! Gibson is a virtual environment based off of real-world, as opposed to games or artificial environments, to support learning perception. Gibson enables developing algorithms that explore both perception and action hand in hand.

Gym Retro

OpenAI

Gym Retro is a wrapper for video game emulator cores using the Libretro API to turn them into Gym environments. It includes support for several classic game consoles and a dataset of different games.

gym-minigrid

maximecb

There are other gridworld Gym environments out there, but this one is designed to be particularly simple, lightweight and fast. The code has very few dependencies, making it less likely to break or fail to install. It loads no external sprites/textures, and it can run at up to 5000 FPS on a Core i7 laptop, which means you can run your experiments faster.

Malmö

Microsoft

Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment. Mamlo Env implements an Open AI "gym"-like environment directly in Python.

OpenAI Gym

OpenAI

A toolkit for developing and comparing reinforcement learning algorithms. This is the gym open-source library, which gives you access to a standardized set of environments. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages.

Osim-RL

Stanford

osim-rl package allows you to synthesize physiologically accurate movement by combining biomechanical expertise embeded in OpenSim simulation software with state-of-the-art control strategies using Deep Reinforcement Learning.