Reinforcement Learning is one of the most exciting types of Artificial Intelligence and the Unity ML-Agents project is one of the easiest and most fun ways to get started. The instructor for the course is Adam Kelly, an experienced Unity developer who has been creating tutorials and courses on ML-Agents for Immersive Limit since the early beta days in 2018. In this course, you will learn how to create intelligent flying hummingbirds that can navigate to flowers, dip their beaks in, and drink nectar. These hummingbirds have six degrees of freedom, meaning they can fly and turn in any direction to find targets. They have more complicated controls and their flight paths cannot be solved with traditional navigation systems. You will learn how to craft a training environment and train neural networks to perform this challenging task, then compete against the birds yourself in a simple mini-game. There's no question that Reinforcement Learning is a complicated subject, but ML-Agents leaves the hard parts to the researchers so that you can focus on solving problems. By the end of the course, you'll have a good understanding of how you can create intelligent agents and integrate them with your own Unity games or simulation projects.

In this tutorial we will learn how to write classes to store a graph with its nodes and edges.

In this lecture students will write the first class for the behaviour tree system that will underwrite the majority of the functionality for the technique.

In this final tutorial on the GOAP system students will a time based trigger for a nurse and then finalise the little simulation by adding a multitude of nurses.

In this lecture students will create the basic structure of a class that will drive the GOAP Agent.

This tutorial introduces the AI technique of Goal-Orientated Action Planning or GOAP. It demonstrates how a powerful behaviour planner can be created by decoupling actions and goals.

In this tutorial students will being building queues to hold game objects as resources that can be used in the planning process.

In this lecture students will learn about the dynamics of crowd behaviour and watch some simple simulations.

In this tutorial students will be challenged to create their own evade method which will see the agent move away from the predicted location of the target.

In this tutorial students will learn about the differences in dynamics between the seek and pursuit behaviours and add an new method for moving an agent into the code.