westbrookt

I think this was the first mention of world models I've seen circa 2018.

This is based on VAEs though.

The DNC essentially gives the neural network external memory. This memory is controlled by read heads, and write heads. These heads are actually just part of the vector output of the neural network. Each of them has a key vector, which is used to find where in memory to read or write. This is done by finding the most similar vector in memory, using cosine distance. The neural network can then use the memory. It also has mechanisms to remember the order that the memory vectors were written in, to allow for sequential recall.

The network itself is trained by reinforcement learning generally. I'm not entirely sure what is done with the parts of the vectors that are not used at a given timestep, when only input and not output is needed.

Again, this is what I believe I was able to understand from the paper. Please let me know if I got anything wrong.

Edit Note: I'm very new to machine learning as well, but this is what I've gathered so far.