Finding great art assets for your games is easier today than ever before.  Great art can be pretty expensive, but there are quite a few free options available where you can get high quality free 3D models, characters, textures, pbr substances, sound effects, animations, music, environments, vehicles, etc…  In this post, I wanted to share a few of my favorites.  For each type of asset, I’ve listed my favorite site to get them free.

Textures

Substance Share – https://share.allegorithmic.com/

I recently learned about this site and plugin and it’s amazing.  Simply visit the site, find the materials you like, download them and drop them into your project.

There are 100’s of materials for just about anything you could want, and they’re all customizable.

Example

Since these are PBR materials, you can modify them easily (even as a programmer with no artistic skills it’s not tough).

Character Models & Animations

Mixamo – https://www.mixamo.com/#/?page=1&type=Character

Of all the resources shared here, this is probably my favorite.  Mixamo was purchased by Adobe a few years ago and decided to change the pricing structure.  All these models & animations were originally for sale at reasonable prices (around $15 per anim), and now they’re all completely free!  There are over 50 high quality characters you can use in your games, and 1000’s of animations.

On top of all the free stuff they provide, the mixamo system will automatically retarget their animations for your other characters.  You can bring characters that you’ve modeled, purchased, or found free in other places right into the site, apply the animations of your choice, and start using them in your game.

Look at all these characters!

And a few of the 1000’s of great animations

 

Sound Fx & 2D Art

OpenGameArt – https://opengameart.org/

This site has a nice variety of assets available.  There are great 2D sprites, free 3D models, music, sounds, textures, fonts, and even text files full of character names you can use.

It’s also my favorite source for free sound effects & 2D art assets.

Particles & Models (and everything else)

Unity Asset Store – https://www.assetstore.unity3d.com/en/#!/search/page=1/sortby=popularity/query=price:0

Of course every Unity developer should be utilizing the asset store.  And while I spend a lot of $$$ on assets, there are also a ton of free assets.  There are over 1509 free models alone!

This is also the best place for free particle systems, none of the competitors come close.  Of course like OpenGameArt, there are a ton of free 3d models, 2d sprites, and everything on the asset store.

Conclusions

Again a lot of these sites have more than I mentioned, but I wanted to focus on the areas where they stand out above the competition.  I’m also always looking for more great resources, so if you have some to recommend, please drop a comment below.

 

I’ve been playing with the new Unity3D Machine Learning system for a few days now and made a little progress.  I wanted to share the steps I found to get a newly created agent setup and trained to complete a basic task.  In this post, you’ll see how to setup a basic agent with the goal of reaching a randomly chosen number using nothing but reinforced machine learning.  We’ll use the new Unity ML Agent system and tensorflow to create and train the agent to complete the task and discuss ways to extend this into a real game AI.

Setup Tensorflow and UnityML

If you don’t have tensorflow setup yet, you’ll need to follow the steps outlined here: https://unity3d.college/2017/10/25/machine-learning-in-unity3d-setting-up-the-environment-tensorflow-for-agentml-on-windows-10/

Video Version

Want to see this all in video?  Here ya go:

AgentML Scene Setup

Once you’ve gone through that process, open the Unity project and create a new scene.

The first thing we need is an academy.  Create a new gameobject, name it “NumberAcademy“.

Add the “TemplateAcademy” component to the “NumberAcademy“.  Our setup doesn’t need the academy to do anything special, so we can start with the basic blank academy provided in the template.

Under the Academy, create another child gameobject.  Name it “NumberBrain“.

Add a Brain component to it.

Set the State & Action size variables to 2.

Set the Action Space Type to Discrete.  We’ll be using 2 discrete actions (up or down) in our project.  We use discrete because these are represented as integers.

Set the State Space type to Continuous.  We’ll be tracking two floats, for state, so we use continuous.

Set the Brain Type to “Player”

Add 2 actions.  Choose any 2 keys you want (I went with A & B), but set the Values to 0 and 1.  The key bound to value 0 decrements the #, the key bound to 1 will increment it.

The NumberDemoAgent Script

Create a new script named NumberDemoAgent.cs

Set the base class to Agent (replace the : MonoBehaviour with : Agent)

Add the following fields:

The currentNumber and targetNumber fields are the most important here.  Everything else is just for debugging and visualizing.

Our agent will pick a random targetNumber and try to get the currentNumber to our target using our increment and decrement commands.

Next we need to override the CollectState method like this:

Here, we’re returning our two floats for current and target number as the state of our agent.  Notice how this matches up with our 2 state variables on the brain and they’re floats which is why we have it set to continuous state instead of discrete.

For our agent to train, we need to select random target numbers.  To do that, we’ll override the AgentReset() method like this:

The final and most important part we need is the AgentStep() method.  This is where we take in actions (aka input), perform some tasks (respond to the actions), and reward our agent for successful choices.

The first thing you’ll see is our text update.  This is only for debugging / visualizing.  It allows us to see the current #, the target, and the # of times we’ve successfully solved the problem (reached the target number).

Next up is the switch where we look at the action and perform our task.  In this case, we either respond to action 0 by decrementing the current number, or to action 1 by incrementing it.  Any value out of that shouldn’t happen, but if we get one, we just ignore it and return.

Then we move our cube based on the currentNumber (using it for the x offset).  This cube again is only for visualizing, it has no impact on the actual logic or training.

We then check the currentNumber against some known limits.  Since we choose a random number between -1 & 1, if we reach -1.2 or +1.2, we can consider it a failure as it’s definitely going in the wrong direction.  In that case, we set the reward to -1 to denote a failure, then mark done as true so the agent can reset and try again.

Then finally, we check to see if the currentNumber is within 0.01 of the target.  If so, we consider that a match, set the reward to 1.0 for a success, and mark it as done.  We also increment the solved counter for debugging purposes (it’s nice to see how many times it’s been successful).

Here’s the complete script:

Setup The Agent

With the script ready, we need to create a new gameobject and name it “NumberDemoAgent”.

Attach the NumberDemoAgent script to it and assign the brain.

Next create a Text object and place it where you can see it (ideally big in the middle of the screen).

Assign the text object to the NumberDemoAgent.

Create a Cube and a Sphere and assign them to the NumberDemoAgent as well (these will help you see what’s going on, much easier than reading #s).

Testing in Player Mode

Now press play.  You should be able to move the cube left and right with your two hotkeys (remember I went with A & B for the hotkeys).

When you get the box to the sphere, it should increment the solved count and reset.  If you go too far the wrong way it should also reset (remember that 1.2 limit).

Training

Once it works in player mode, select the brain and change the “Brain Type” to “External”

Save your scene and build an executable where the scene is the only thing included (with debug mode enabled).

For your output folder, choose the python subdirectory of your ml-agents project (included when you downloaded or cloned the source project).  For example, mine is located here: C:\ml-agents\python.

Remember the name you give it, you’ll need that in just a minute.

Anaconda / Jupyter

Launch an anaconda prompt.

Change directory to the python folder you just built into.  ex. “cd c:\ml-agents\python”

Enter the command “jupyter notebook” (you may need to hit enter a 2nd time btw)

You should be prompted with a web interface shortly after that looks like this:

Change the highlighted parts to match.  On the env_name, don’t just put in “numberdemo”, use the name that you built your executable with.  Buffer_size and batch_size you can copy though (it’s important to note that these #’s were only found by testing/trying, even after getting it working, I still barely understand what’s going on with them).

Once you’re done editing the hyperparameters, run the steps in order.

Start with step 1 & 2.  (the * disappears and a # appears in the [*] when it’s done.

When you run step 3, you should see a window appear for your game (a small window though).  The first time, you’ll probably also get a windows permissions dialog, make sure to allow it.

Once you start step 4… WAIT.. and watch the results come in (first one may take a minute so be patient)

Once it’s saved a couple times, hit the stop button.  Then move on to step 5 and run it.  This will export your training data to a .bytes file in the “python/models/ppo” subfolder.

Copy the .bytes file (again it’ll be named to match your executable name) and place it in your Unity project somewhere.

Select the brain and set the “Brain Type” to “Internal”.

Assign the .bytes file to the “Graph Model” field.

Save and press play!

Conclusions

This is a pretty simple sample, meant to just help get a basic understanding how how this system works.  I’m excited to see where it goes though and build bigger more interesting projects to control game AI and make interesting gameplay / bots.

 

Reference

Unity ML Agents GitHub – https://github.com/Unity-Technologies/ml-agents

HyperParameters Doc – https://github.com/Unity-Technologies/ml-agents/blob/master/docs/best-practices-ppo.md

Machine Learning Playlist –https://www.youtube.com/watch?v=qxicgknzUG8&list=PLB5_EOMkLx_Ub1A4iHoDUx7vg37sVoL-E

 

I’m extremely excited about the new Unity3D Machine Learning functionality that’s being added.  Setting it up was a little painful though, so I wanted to share the steps I followed, with the specific versions that work (I tried a whole lot and nothing else worked).  In this guide, I’ll show you everything you need to get setup and ready to start with the 3D ball example.  There’s also a video version at the end..

Installing the CUDA toolkit

You’ll need to download CUDA 8.0.61 for this to work.

You can view the CUDA download archive here: https://developer.nvidia.com/cuda-toolkit-archive

Select and download CUDA Toolkit 8.0 GA2

Run the Installer

Choose Express

CUDNN Setup

Next we need to grab the CUDA Deep Neural Network library aka cuDNN

Visit the CUDNN page here: https://developer.nvidia.com/cudnn

You’ll need to create an NVIDIA account and log in to download the library.  It’s easy to do and free though.

Once you’re logged in, click the download button.

 

Choose the download link for v6.0 for CUDA 8.0 – Download cuDNN v6.0 (April 27, 2017), for CUDA 8.0

Copying the CUDNN Files into the Toolkit Folder

Open the cuDNN zip file.

Copy the 3 folders (bin, include, and lib) from the zip file into your CUDA 8.0 folder.

The default folder path you’d copy into is C:\Program Files\NVIDIA GPU Computing Toolkit\cuda\v8.0

 

Environment Variables & Path

Now you need to add an environment variable and two path entries.

Open the environment variable dialog (hit the windows key and start typing envir…. and it’ll auto popup for you)

Click the Environment Variables Button

Click the New button to add a new System Variable

Set the Variable Name to CUDA_HOME

Set the Value to C:\Program Files\NVIDIA GPU Computing Toolkit\cuda\v8.0

Editing the Path

Find the Path in the Environment Variables Dialog

Make sure you select the System variables version, not the user variables!

Click Edit

Add the following two folders to the path.

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\extras\CUPTI\libx64

Click Ok a couple times to close out the dialogs.

 

Anaconda Setup

Next, we need to install Anaconda to configure our python environment.

Visit the download page here: https://www.anaconda.com/download/

Download the 3.6 version (I went with the 64 bit installer, not sure if it matters but I’d go with that one).

Run the Anaconda Installer and choose the default options all the way through.

 

After the installation completes, open the Anaconda Prompt

Creating the Conda Enviroment

Next, we need to create an environment with python 3.5.2.

Run the conda create command in the prompt like this:

conda create -n tensorflow-gpu python=3.5.2

Next, activate the newly created environment with this command:

activate tensorflow-gpu

And finally, install tensorflow with this command

pip install tensorflow-gpu

 

Once the installation completes, you can test that it was successful by launching python (still from that anaconda prompt) by typing:

python

Then use the command:

import tensorflow as tf

 

Downloading the Sample Project

To try out the ML agents, you’ll want to download the sample project from the GitHub page here: https://github.com/Unity-Technologies/ml-agents

You can download the zip or use GIT and clone the repository.

Next, open another anaconda prompt as an administrator.

Change directory into the ‘python’ subfolder or the location you cloned or unzipped the project into.

For example, my folder is: C:\Git\ml-agents\python because I cloned the repository into my c:\git folder.

Fixing Html5lib

Next, type the following command: conda install –force html5lib 

Now type: pip install .

Next Steps

If there are no errors, you’re good to go and ready to start setting up your Unity project with tensorflow and machine learning!

Next, you’ll want to build the Unity Environment.  The steps for this are very clearly laid out here so I won’t repeat them: https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Getting-Started-with-Balance-Ball.md

One issue I ran into though was the ENABLE_TENSORFLOW define being cleared out after I installed the tensorflowsharp library.  When this happened, the “Internal” option disappeared.  Simply re-add it and hit play so it re-compiles, then the internal option should re-appear.

Special Thanks

First I wanted to say thanks to the guys at Unity for building this all out.  I’m excited to start integrating machine learning into projects for my AI.

I also wanted to thank Nitish Mutha for this awesome blog post that got me 90% of the way through this setup.

Video

 

Scaling a player in VR opens up a world of options for game design and can be a ton of fun.  In this example, I’ll show you how to quickly adjust the scale of your player, make them shrink and grow at will with a couple lines of c# in your Unity3D project.  We’ll use SteamVR and the CameraRig to make our player tiny, walk through a little door they couldn’t normally get into, then scale them back up on the other side.  Recreate it yourself or download the project sample.

Video

Project Source

Download the project source here: https://unity3dcollege.blob.core.windows.net/site/Magnets/VRScaling.zip

 

Ball Passing System for Unity

This is a simple system I’ve used to pass soccer balls, hockey pucks, and anything else you can imagine in a sports game from one player to another.  The passing script contains everything needed to make something like this work and the logic for calculating the closest angle to your aim is pretty straightforward (and included in the project source below)

 

Video

Project Download

https://unity3dcollege.blob.core.windows.net/site/Downloads/Passing%20Balls.zip

Build your own snap to grid system in minutes with this project as a starting point or sample.  You can create a Unity3D Grid based game easily by creating a single component responsible for giving your project the correct grid coordinates.

This basic grid system works by taking in a vector3 and providing the nearest point on the grid to that position.  Your code only needs to call into the Grid class to get that location, with no other modifications.  The grid can be resized or offset by simply moving it around in the scene, and you can visualize the layout using OnDrawGizmos as seen in the video.

 

Video – Unity3D Grid

Source Code

 

Project Download

https://unity3dcollege.blob.core.windows.net/site/Downloads/Snap%20To%20Grid.zip

 

Need to snap your gameobjects to the ground?  No problem, with this simple editor script you can place objects easily with a quick hotkey

Video

Code

C# delegates and events are extremely powerful and understanding them will make your life as a Unity developer much easier.  Luckily, they’re pretty easy to learn and you can start incorporating them in your projects today.  In this 2 part post, I cover the basics of delegates and how they work, then lead into how you can use delegates as callbacks for your coroutines.  With this technique, you can launch a coroutine that can perform whatever action(s) you choose when it’s done, all in a clean and reusable manner.

Part 1 – Delegate Basics (4 mins)

Part 2 – Delegates as Callbacks for Coroutines (4 mins)

Conclusions

Using delegates can make your project clean and extensible, removing the need for endless if statements, switches, etc.

Video 1 – Interfaces

 

Video 2 – Abstract Classes