7thSage here again, welcome back to part 3 of the Godot Tactics tutorial. Sorry about the long delay. Life has been fairly hectic lately.
Hey readers, I am curious how you all feel about the future of Unity. Has their recent changes in pricing changed anything for you? I know that they have backtracked a bit, but I personally have lost trust in them and don’t feel that they have done enough to earn it back. Unity has been a great tool for me for many years, but I am curious if it is time to start looking at other options. What do you all think? Should I stay with Unity or try something else like Godot?
Also, I’m just curious how many of you have been following along with my most recent blog project and if so what your thoughts are. Feel free to leave comments below, I would love to hear from you.
It has been a while since my last post, and you may need to wait a bit longer still. With the arrival of summer I have had a lot happen all at once, family moving, a wedding, vacation coming up etc. This is only a quick post so you all know I am still here and to give a quick preview of what is coming next…
In this lesson we will be creating a system that will let us easily persist the entire object graph of our game.
Every time I start a new project, I spend a fair amount of time reflecting on pain points from previous projects, what I enjoyed working with, things I would still like to try, current standards and patterns, and how my fellow engineers might react to something I write. One of the big problems every project faces: how to access what you need, from where you need it. I’ve done a bit of experimenting and decided to share something I think is worth further exploration.
Most projects you will create in Unity are probably intended to be interactive. They should be able to respond to mouse clicks and drags, keyboard, touch, or other forms of user input. This tutorial will cover a variety of options by which you can manage these types of events.
Unity’s ECS architecture is nearing its official release, and I am getting more and more excited for it. After a brief introduction, we’ll spend some time digging in and experimenting. By the end you should have an elementary grasp of Entities, Components and Systems within this new architecture.
A Q-Table greatly simplified the challenge of helping a computer agent “learn” to solve an environment. Unfortunately, this particular approach doesn’t scale well to the kinds of applications I would like to create. To help overcome this next hurdle, we will raise the complexity a bit more as the Frozen Lake environment is approached again, this time by using a neural network.
Telling a computer to perform an action based on an input isn’t too hard. Teaching a computer to learn what action to take based on what it sees is a whole different challenge. Now imagine that the computer wont even know if the action is good or bad until some unknown point in the future – how hard would that be? Well, let’s find out as we take a look at a machine learning algorithm called Q-Learning.
Machine Learning provides us an interesting way to solve special kinds of problems. If you’re just playing around, you may see that creating a good problem to work with can be a lot of work on its own. OpenAI gym has recognized this challenge and provided a great solution. They have created a whole collection of different “environments” that are perfectly suited to machine learning. To help us get started, we will be looking at one of the easy challenges which we can solve using Q-Learning.