@didier I am unable to get to YouTube ( due to local network configuration and rules ) and I am probably going to be too busy to do a whole lot of development around the ecosystem, though I am interested. 
I was just wondering if you had the source code for your logic nodes and maybe the blend for your tank game training example somewhere public like GitHub. So that I can test using it for game AI.
@zicklag It will be possible to use it soon I hope (time is something fast as for you) , throught a website I will build soon too with doc user.
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Last step was the export of a Neurone Network trained into ATRAP, from one machine to another.
It opens the door to using a crowd of PC/ATRAP with Neural Networks able to exchange their NN during training.
Then as it’s easier to train the Neural Network on small portions of a 3D environment, the next step is to parralelize/combine/merge several neural networks trained into differents 3D environments into only one, thus having the equivalent of a training done on a vast 3D world.
More generally, it’s a way to :
-
to accelerate the creation of a community / ecosystem capable of exchanging their best Neural Networks during a “Distributed Neural Network Training” … like a kind of electrical network on which you can get the best energy at any time for your AI/Neural Network.
-
to distribute among several actors of an ecosystem, the training of AI in the 3D environment ATRAP/ARMORY 3D :
. some in charge of creating a portion of the 3D environment on Armory 3D.
. others in charge to specialize in training Neuronal Networks on ATRAP.
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Ya, It is quite easier to train the Neural Network on the 3D environment, the next step is to merge neural networks trained into different 3D environments into only one. Thanks for providing a solution. By the way, anyone knows how to fix epson error code 0x97 , Its troubling me.
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@nimmy I made the test with the export of the NN then import in html environment … json format. Everything then possible !
Actually I focuse on training an industrial robot … meet some problems when IK is good in blender and but then fail in Armory/Krom … worse in Html. With the new today version hope that will change.
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It takes time for things to mature and we have an interesting post of @MagicLord here Procedural animation and Motion matching that could reuse the approach used for ATRAP to find a new way for making animation with less efforts in Armory.
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Any progress so far ?
@Chris.k
I’m on the 2nd demo part with industrial robot. The IK in Armory needs more work …
A great progress is that the Forum site is starting to be activated on the animation part in Armory… perhaps the beginning of the creation of an ecosystem between Armory/Animation/IA.
The links you gave here in this post Procedural animation and Motion matching was a starting point/motivation for starting ATRAP.
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Some news on actual progress: it seems promising to use ATRAP to finaly make an IKNN (Neural Network based on Inverse Kinematics) for a robotic arm, hoping to be faster and more accurate that actual existing solutions, as for example with Fabrik or Jacobian, methods that need CPU ressources and could be impossible to use in case of lot of objects with IK to manage in a game scene on a small computer.
For your info, ATRAP generates the training data for the neural network, using random joint quaternion values during firsts stages, then using NN results as training progreses. The reward is fonction of the distance to the target.
Thereafter a use case where reaching correctly the target is crucial …
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Dude that’s awesome! IK is a great challenge to try to solve with ML and something that we could really use in Armory.
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@Monte_Drebenstedt I come back to your interesting remark about
The idea of a game with having NN embedded inside characters, for me could consist for example of creating a training environment for each category of characters equipped with a NN and then launching them into an operational theater to observe what’s happening …
Taking for example a game near a Age of Empire, but which allows you to form the NN of different characters using different kinds of training environments.
Game tools calling Armory game engine, are used to create/adapt the game training environments.
Once your characters trained, then you launche them on a territory and they start to create by themselves the development of a civilization.
According to the evolution of the different civilizations, the game consists in knowing how to reinforce your civilization by improving the formation of the NN for your different categories of characters.
At the same time, NN of characters could improve too through the experiences/rewards obtained during the evolution of a civilization. Thus the choice could be for the gamer to let the NN improves by themself, reuse/import best NN into training environment, best NN initialisation, adapt rewards in training, …
This approach could be seen too like in games similar to Virtual Regatta for example, to allow you to train a NN for a racing boat and see what’s hapenning to it when racing into a Virtual Regatta. Same thing for car race, skying, foot, ops, …
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When will you have some game example good AI in Armory to show ? LOL
@MagicLord Please look here for first tests with NN training in the simple tanks game template that you know https://youtu.be/zlhQTxwBSnQ or https://youtu.be/ef-S7M6_yEo
The 2 main logic nodes at the center of the NN training
and 
Is it not enough in your opinion to respond to your remark, as I interpret your remark as the need to have a feasibility study result / demonstration model to show 
?
(My hope is to create motivation of some passionates to use this technology that I encapsulate through the name ATRAP (actually kick and dirt that I am busy at reusing and adapt/optimizing it for a test on Inverse Kinematic for Robot arm ), and to give the help needed for an open-game that must be defined see the discussion opened by @trsh here Ongoing projects that need help… sharing ideas and feasability welcome)
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I didn’t know you were really working on some logic nodes lol
Looking at video, the graph seems very spaghetti , could not NN encapsulate more things ?
For example one NN specifically made for character , another for some vehicule type ?
It is promising, just need to see in action in game and see if it could be useful.
The game example you posted, i’m not sure it will be able to at least perform the same AI like other AI systems ( Behaviour tree, hard coded conditions, FSM).
For example :
- see player , player distance
- hear player on radius
- obstacle avoidance
- search cover or best place to attack
- retreat and search for health pack
- choose to run to pick up a attack power up
- team up with other ai to do strategy like suppression fire or flank
I mean something more practical and more ai advanced.
Well, i don’t need NN, but i think it could be very usefull for lot use cases.
The best point is Armory does not have Behavior trees or FSM or any other AI systems,
so your logic nodes would be very helpful to help beginners and other users to get AI quickly running with some logic nodes without even needing to know AI programming 
This could expand and make Armory even more interesting.
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Yes, it could simplify a lot of works.
The role of the Neural Network is to replace the need to set specific algorithms belonging to those kinds of specific behaviors that you list. All you need is to let your NN plays, learns from it’s loose or win.
For exemple, considering the game tanks, an action gives it a reward or not and the NN learns what actions give best reward only using the picture taken by only one camera in the scene.
The image could be other sensors, or for example in the case of robot, I actually use angles/quaternions in the robot arm.
What is interesting too, is to discover the capacity that our NN has to imagine new scenarios that noone envisages before.
What I observed too during training with tanks is that the NN learns quicker if you help it to win … for example by moving the other tank/target at a good place for getting a hit. It doesn’t matter for it if you moved or not the target. So I think it’s an example of what we will discover with NN inside games in the futur, and creating a good training could reveal a good experience of the game situations. How to win could become how to better train my NN.
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Specific behaviour is made to put rules on the game AI , so the player knows how to play and how to get advantage of AI flaws.
There was some people making a military simulation, it was so realistic , most of the time the player lost and the game became frustrating so a bad game.
It’s easy to make some game with AI always finding player and never miss, that’s game people don’t play lol
But i think NN can be used in a way the difficulty can be tweaked.
Well, i hope you’ll make a good demo game AI we could play and see if the AI is good enough.
This could be interesting, for example Metal Gear Solid game AI.
@MagicLord
I also share your opinion that a good game isn’t made by a super IA, too performant. I think too that it’s important in those examples of shooting games with “behaviour trees” and “goal oriented action planning” to keep a good feeling of combat.
I propose the use NN applied to another type of gameplays, more in the spirit of Age of Empire, at the limit like WorldOfWarships, or for games where you try to accomplish complex mission task.
My proposal is elsewhere. Take the image of a chess game or football game, but where you don’t move your pieces with your mouse but let the pieces of the chessboard/field equiped with NN to decide itselfs where to move. The interesting part of the game would be how to obtain the best training of the NN. Your are in the position of the manager/teacher of your team.
It could be too for example a game where your engineer increasingly more complex ROVs by training their NN for increasingly more complex mission tasks, inspired by what we find here with existing underwater robots competitions (https://www.marinetech.org/rov-competition-2/) or by escape rooms.
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Info about robot arm kinematics progress 
: Thereafter a short gif giving an idea of the robot during it’s training (**see explaination at bottom).
Random data of joints angles are sent as an action to the robot arm at the beginning of the training, then as the training progresses, values from the NN replace those random values. Thus during the training, we see the robot arm more and more often finding the right position of the arm in order to reach the given target.
** (In a Forward kinematics problem, the location of the final end cap of the robot arm (Effector) in space, i.e. the position and orientation of the entire arm, is determined according to the joints angles. If you consider a position and orientation of the Effector that reach a Goal, the Inverse kinematics problem is to find the values of the joints that allow the arm to reach this given location. There are various ways to determine the solution of the Inverse kinematics problem and in Armory you have the Forward and IK Node at your disposal, but I invite you to test and verify that it’s not perfect. The NNs are capable of representing those non-linear relationships between input and output data. It’s already demonstrated with Tanks Armory/Atrap, and here the NN is learning through a try/reward mechanism that provides the link between cartesian and joints space required by the Inverse kinematics problem.
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The news 
: realization of a 3D data visualization of the robotic arm training.
The neural network is represented to follow its evolution during the training of the industrial robot.
Each point is a neuron and the color (inspired by the colors of infrared cameras) represents the synaptic coefficients.
The next evolution of this data visualization will integrate the evolution of the Loss during the robot training.
The network is built automaticaly according to the Atrap parameters and using spwan objects, unique color to each one… then a challenge to make it without wasting too much of the PC “I5 CPU/windows10” ressources.
The first tests have been launched and will help to better understand how a neural network works, which is still a vast area of research today.
I have a feeling too that results obtained in maths for nuclear physics to represent certain things will be useful to me, as well as some of the processing used for radar systems.
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