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3 Reinforcement Learning Environments Every ML Enthusiast Should Know

aribornstein profile image PythicCoder Originally published at Medium on ・4 min read

Reinforcement learning is the study of decision making over time with consequences. The field has developed systems to make decisions in complex environments based on external, and possibly delayed, feedback.

Reinforcement Learning - Personalizer - Azure Cognitive Services

“Microsoft Research, works on developing the theory, algorithms and systems for technology that learns from its own successes (and failures), explores the world “just enough” to learn, and can infer which decisions have led to those outcomes. Our primary goal is reinforcement learning in the real world: understanding how to build systems that work, even when simulation is unavailable and samples are scarce.” — Microsoft R&D Reinforcement Learning Team

To celebrate hosting the Reinforcement Learning IsraelMeetup organized by the talented Shani Gamrian at the Microsoft Reactor here is a list of three Reinforcement Learning Environments every ML enthusiast should know.

You can get a free subscription for Azure to get started playing with each of these environments below

Create your Azure free account today | Microsoft Azure

If you have any questions feel free to tweet to @pythiccoder.

1) Text World

TextWorld is an open-source, extensible engine that both generates and simulates text games.

You can use it to train reinforcement learning (RL) agents to learn skills such as language understanding and grounding, combined with sequential decision making.

microsoft/TextWorld

You can check out the code for getting started with Text World with the link above.

2) AirSim

AirSim as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.

AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open-source, cross platform, and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. Similarly, we have an experimental release for a Unity plugin.

Code can be found here:

microsoft/AirSim

3) Project Malamo

The Malmo platform is a AI experimentation platform built on top of Minecraft, and designed to support research in artificial intelligence.

Project Malmo sets out to address these core research challenges, addressing them by integrating (deep) reinforcement learning, cognitive science, and many ideas from artificial intelligence.

Project Malmo - Microsoft Research

Minecraft is ideal for artificial intelligence research for the same reason it is addictively appealing to the millions of fans who enter its virtual world every day. Unlike other computer games, Minecraft offers its users endless possibilities, ranging from simple tasks, like walking around looking for treasure, to complex ones, like building a structure with a group of teammates. Code for Project Maleo can be found with the link below:

microsoft/malmo

Bonus Production RL @Microsoft

In my role as an AI Cloud Advocate I’m often asked for production examples of Reinforcement Learning, Azure provides a custom recommendation service if you are interested in a production example of reinforcement learning strongly suggest checking it out.

Personalizer documentation - Azure Cognitive Services

Next Steps

If you haven’t yet join the group and check out the Global Microsoft Reactor Events page for similar events across the world

About the Author

Aaron (Ari) Bornstein is an avid AI enthusiast with a passion for history, engaging with new technologies and computational medicine. As an Open Source Engineer at Microsoft’s Cloud Developer Advocacy team, he collaborates with Israeli Hi-Tech Community, to solve real world problems with game changing technologies that are then documented, open sourced, and shared with the rest of the world.


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