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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and basic IT and Linux skills seeking to transform games into environments with ML and deep learning with Tensorflow, Keras, & Unity

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who wish to transform games into environments using machine learning and deep learning with Tensorflow, Keras, and Unity.
  • Skill-level: Foundation-level Learn Unity ML-Agents skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This course takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the course, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Unity ML-Agents instructor, students will learn about and explore:
  • How to apply core machine learning concepts to your games with Unity
  • The Fundamentals of Reinforcement Learning and Q-Learning and applying them to your games
  • How to build multiple asynchronous agents and run them in a training scenario
  • Developing Reinforcement and Deep Reinforcement Learning for games.
  • Understanding complex and advanced concepts of reinforcement learning and neural networks
  • Various training strategies for cooperative and competitive agent development
  • Adapting the basic script components of Academy, Agent, and Brain to be used with Q Learning.
  • Enhancing the Q Learning model with improved training strategies such as Greedy-Epsilon exploration
  • Implementing a simple NN with Keras and use it as an external brain in Unity
  • How to add LTSM blocks to an existing DQN
  • Building multiple asynchronous agents and run them in a training scenario

Course breakdown / modules

  • Machine Learning
  • ML-Agents
  • Running a sample
  • Creating an environment
  • Academy, Agent, and Brain

  • Reinforcement Learning
  • Contextual bandits and state
  • Exploration and exploitation
  • MDP and the Bellman equation
  • Q-Learning and connected agents

  • Installing Python and tools
  • ML-Agents external brains
  • Neural network foundations
  • Deep Q-learning
  • Proximal policy optimization

  • Agent training problems
  • Convolutional neural networks
  • Experience replay
  • Partial observability, memory, and recurrent networks
  • Asynchronous actor critic training

  • Multi-agent environments
  • Adversarial self-play
  • Decisions and On-Demand Decision Making
  • Imitation learning
  • Curriculum Learning

  • What was/is Terrarium?
  • Building the Agent ecosystem
  • Basic Terrarium Plants and Herbivores
  • Carnivore: the hunter