So this last month there were two major developments for AOgmaNeo.
First, there is a new interactive demo hosted on the Ogma website that you can try. I compiled AOgmaNeo with Emscripten (WebAssembly), and it runs pretty nicely in the browser. The demo is a real robot that was then turned into an web demo by learning a "simulator" for it with AOgmaNeo (simply by observing its response to motor commands through a camera). It's a fun demo that showcases the world-modeling capabilities of AOgmaNeo, give it a try! I called it "real2sim", a reversal of the more common "sim2real" paradigm in machine learning. It still uses the dual-encoder setup discussed in previous blog posts, but we now find that that is not needed, leading to the next point.
Second, new findings show that the dual-encoder setup is actually not strictly necessary. With the inclusion of a new "importance" setting for each IO layer, one can now manually set how much the hierarchy pays attention to certain inputs. This was previously done automatically by the error-driven encoder, but a simple manual setting is more general and can perform tasks the error-driven encoder could not. So, I have released a new version of AOgmaNeo that goes back to the faster single-encoder setup, but included a new function called "setImportance" (there is also "getImportance") that allows the user to control how important an input is to the encoders.
Thinking about what's next, I think I may want to change the allocator used in AOgmaNeo. The memory it uses is static (minus some minor things in the interface), and is heap-allocated when init is called. I feel like I can just use a memory arena here, although it will probably not make much difference it feels like a cleaner solution. I am also working on a new branch that introduces a new algorithmic optimization I call "the topology optimization". This new optimization also has the side benefit of being more cache friendly, due to the simpler memory access pattern.
That's all for now!