![]() ![]() All this links is multplied by a weights, So when you insert value in the input, you get value come out the output and the output result depends of the values of the weights. Each inputs is linked to all hidden neurons and hidden neurons to ouput. Neural Network is a series of inputs, ouputs and between them "hidden neurons". Is in the family of search space algorithm? I use neural network and genetic algorithm. Not sure is the same thing you talked about. Though all this takes should be to subtract the added offset at the start from the end position again, and then you can use the same formula. Obviously you'll need to slightly change that definition if you want to keep your program as-is with all the walkers of one generation spawning in a line. This should be even better than having to do a bunch of sphere collision checks. High scores mean, the average distance to any previous walker is high, so the found location is particularly novel. Instead of telling the walkers "walk further", it tells them "end up somewhere where nobody else has been before." So sum the distances of the endpoints of all previous walkers to the end point of the current walker and divide by the number of walkers. The novelty score of your walker is the average distance you walker ended up at from any previous walker. In case of a walker like what you evolved in the first two videos: A simple measure they are using is "sparseness" of the explored areas. Video of pacmans and ghosts (co-evolution where the progress of one help to the other to progress further )Īh, I read up on it now. Download the module here ( but no support provided for now ). ![]() Here is a couple of link about my test about this. For now saving and loading network in a file function is not avalaible ( but can be coded on python side ). The goal of this is when you get the result you want, you are be able to save the trained network in a file and re-use it where you want (game) and not to have to training it again each time. For quadruped and biped video it's about how far they can go without the body touch the ground. In p acman video, how many yellow point they catch and how far they stay away from ghost. For example: in the pole balancing video, each players are rewarded on how long the pole stay straight and how far it's stay from the border. In all videos, all "players" learn by itself. I do a lot of try at each new type of simulation but when you start to get result it's very impressive. I get some difficulty at this time to understand all this the first time. What I learn is it's more difficult to find good parameters, right inputs to feed to the module and how to " reward " good behaviors then code the module itself. So from that, I'm starting to code a python module with Cython. I found a popular tutorial about this on internet. All my curiosity coming from my brother that talk to me about learning machine algorithm. I love this stuff.Īfter getting the result I want with Molecular and waiting for a "mesher" be available in Blender, I'm starting to have fun (and try something new) with Neural Network and Genetic algorithm. What do you get when you use Python and the Blender Game Engine to teach systems to perform tasks? Fascinating videos of creatures learning to walk, stumble and get up again. ![]()
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