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Reinforcement Learning
Today there exists a large number of platforms that can be used for the creation of an
environment. Some popular ones are:
• OpenAI Gym: It contains a collection of environments that we can use to
train our RL agents. In this chapter, we'll be using the OpenAI Gym interface.
• Unity ML-Agents SDK: It allows developers to transform games and
simulations created using the Unity editor into environments where
intelligent agents can be trained using DRL, evolutionary strategies, or
other machine learning methods through a simple-to-use Python API. It
works with TensorFlow and provides the ability to train intelligent agents
for 2D/3D and VR/AR games. You can learn more about it here: https://
github.com/Unity-Technologies/ml-agents.
• Gazebo: In Gazebo, we can build three-dimensional worlds with physicsbased
simulation. Gazebo along with Robot Operating System (ROS) and
the OpenAI Gym interface is gym-gazebo and can be used to train RL agents.
To know more about this, you can refer to the white paper: https://arxiv.
org/abs/1608.05742.
• Blender learning environment: It's a Python interface for the Blender game
engine, and it also works over OpenAI Gym. It has at its base Blender: a free
3D modeling software with an integrated game engine. This provides an easy
to use, powerful set of tools for creating games. It provides an interface to
the Blender game engine, and the games themselves are designed in Blender.
We can then create the custom virtual environment to train an RL agent on a
specific problem (https://github.com/LouisFoucard/gym-blender).
• Malmö: Built by the Microsoft Team, Malmö is a platform for AI
experimentation and research built on top of Minecraft. It provides a simple
API for creating tasks and missions. You can learn more about Project Malmo
here: https://www.microsoft.com/en-us/research/project/projectmalmo/.
We will be using OpenAI Gym to provide an environment for our agent. OpenAI
Gym is an open source toolkit to develop and compare RL algorithms. It contains a
variety of simulated environments that can be used to train agents and develop new
RL algorithms.
The first thing to do is install OpenAI Gym, the following command will install the
minimal gym package:
pip install gym
If you want to install all (free) gym modules prefix it by [all]:
pip install gym[all]
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