- [[Learning the identity map]]
- It was found that one-hot vectors of arbitrary dimension can be embedded in 2-dimensional space in a form that is identifiable by the perceptron
-
Discrete states can be represented by one-hot vectors
-
This means,
-
The state is considered a two-dimensional vector.
-
I had a related thought a long time ago: Vectorization of states.
-
At this time I was thinking of constructing a direct
-
Why did you want to state vectorize?
-
I wanted the state transitions to be acquired and improved through learning.
-
This approach is to create training data from a given state transition diagram and train it with a multilayer perceptron.
-
I want to replace existing state transition diagrams written in hard code with neural nets.
-
This is the same concept as in [Replace conditional clauses in if statements with 2-class classification
-
-
State usually [recurrent
-
If this recurrent part of the system does not need to be human observable, there is no need to make it discrete.
-
Then you can turn it in a compressed vector state.
-
Reinforcement learning for continuous state may also be relevant
-
This page is auto-translated from /nishio/状態のベクトル空間への埋め込み using DeepL. If you looks something interesting but the auto-translated English is not good enough to understand it, feel free to let me know at @nishio_en. I’m very happy to spread my thought to non-Japanese readers.