EDeN Server comes to unity!
EDeN hooks up to Unity!
Both EDeNServer EDeNCore projects previously interfaced to a python runtime through a dll.
With an experimental DLL into the lesser known Unigine engine for the server.
Now with an asynchronous (DLL Internally managed) connection from the EDeNServer, Unity is now fully compatible to host both training and inference of your EDeN entities!
– This something ML Agent through tensor flow (https://github.com/Unity-Technologies/ml-agents) has yet to achieve!
I will producing an experimental package on the unity store in the future!
For now I simply ensure both ‘EDeNServerInterfaceDLL’ and ‘EDeNDataServerNETUnityDLL.dll’ are under a Plugins folder.
This is early days for a commercial product, but I hope to build up demonstrations for all to play with sooner rather than later.
EDeN 2.0 (10th revision) accepted by Arxiv!
I am pleased to announce the 10th revision of the paper is now available on Arxiv!
Stay tuned for code demonstrations and training results!
Click here for Arxiv page and download!
Energy Decay Network (EDeN)
This paper and accompanying Python/C++ framework is the product of the author’s perceived problems with narrow (discrimination based) AI (Artificial Intelligence). The framework attempts to develop a genetic transfer of experience through potential structural expressions using a common regulation/exchange value (‘energy’) to create a model whereby neural architecture and all unit processes are co-dependently developed. These expressions are born from fractal definition, stochastically tuned and managed by genetic experience; successful routes are maintained through global rules: Stability of signal propagation/function over cross functional (external state, internal immediate state, and genetic bias towards selection of previous expressions). These principles are aimed towards creating a diverse and robust network, hopefully reducing the need for transfer learning and computationally expensive translations as demand on compute increases.