Complete Guide to Unity Machine Learning Agents (Basics)

Implementation of reinforcement learning environment,
Easy and convenient with Unity!

reinforcement learning environment ,
How should I prepare it?

Since AlphaGo's groundbreaking performance in 2016, interest in reinforcement learning , known to have been applied to AlphaGo, has grown significantly, and the enthusiasm remains strong. The key components of reinforcement learning are the reinforcement learning algorithm and the reinforcement learning environment, as shown below. These two components exchange information, including actions, states, and rewards, allowing the reinforcement learning algorithm to learn.

Since AlphaGo, reinforcement learning algorithms have made significant progress. In response, a variety of reinforcement learning environments have been released, including OpenAI GYM, Mujoco, Atari, GTA5, and Malmo. Most of these environments are game-based. While reinforcement learning is clearly an ideal algorithm for games, there has been a recent surge in attempts to apply it beyond games to diverse fields, including recommendation, robotics, drones, energy, and finance.

However, reinforcement learning environments for these diverse fields are still lacking. In particular, it's extremely difficult to expect an environment that precisely meets developers' specific requirements to be released. Even if you have a robot environment with a specific sensor configuration and joint structure that you want to apply reinforcement learning to, it may be impossible to even begin research without a publicly available reinforcement learning environment for that field.

If you use an environment that has already been created,
There are these drawbacks:

About the environment
Sujeong
difficulty

For each environment
How to use this
difference

necessary
The environment
There may not be any

But in September 2017, Unity, one of the world's largest game engine companies, released a tool called Unity Machine-Learning Agent that could solve this problem.

With Unity ML-Agents
Implementing reinforcement learning environment!

In this lecture, you will learn how to implement various reinforcement learning environments using this Unity Machine Learning Agent, as well as the theory and code implementation of reinforcement learning algorithms applicable to those environments.

Information before taking the class!

This course's content is identical to the book "Reinforcement Learning with PyTorch and Unity ML-Agents." Please be aware of this before attending.

Mastering Unity Machine Learning Agents - Basics

The entire "Complete Mastery of Unity Machine Learning Agents" course will be divided into two sections: Basics and Applications. This lecture will cover the Basics section. The specific content covered in the Basics section is as follows:

• Reinforcement Learning Basics and Theory
• Unity Installation and Basic Usage
• Unity Machine Learning Agents Installation, Component Description, and Usage (mlagents-learn, Python API)
• Environment Creation
  • GridWorld, Drone, KartRacing
• Learning reinforcement learning algorithm theory and implementing code
  • DQN, A2C, DDPG, Behavioral Cloning

The code for the environment we will create and the algorithms we will learn in this lecture is all included on GitHub .
The images below are the reinforcement learning environments you will implement in this lecture and the results of learning using the reinforcement learning algorithm you will implement.

_{Creating a Gridworld Environment}

_{Creating a drone environment}

_{Creating a kart racing environment}

Frequently Asked Questions
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