@@ -111,6 +111,10 @@ The idea is to craft a best-practices-and-all-the-priors model and controller fi
# Log
## 2020 05 06
I expect the *result* of the control-with-search experiment will be that success does not look like replacing the control algorithm, but with searching for parameters in the simulation used to to prediction. At least - that aspect in realtime. Since time for simple systems becomes essentially free when we simulate them, we can use these simulations to search for controllers over thousands of 'epochs' - then we just have to plug them into our physical systems ... their 'digital twins' - so, the pain there is in aligning system parameters. However, prediction here works on a much faster time step: we have the last-state, and a prediction of the next state, given our simulation. We also have a physical equivalent of this. Say we just input control variables open-loop into the physical system, read its state, and (we are potentially doing this near or upwards of 1kHz), work to match *our* predicted next-ms-state to the system's real next-ms-state. So we can do fast simulation / physical alignment, and then lean on the free time in the simulation to learn higher order control. Perhaps, or likely, that a *good* implementation of these mixes both, so that it's always doing both - we can imagine the computer sort of every-so-often receiving 'real' data, using that to check / match its model against the world, and then (in the remaining cycles inside of the ms: 4 000 000 on a 4Ghz clock), continue to imagine possible futures / trajectories / control parameters. OK.
## 2020 04 05
I've turned all of my hardware and frameworks back on after ~ 1 mo away from code, so I'm just note taking on what my steps are for this thing, and some other observations.
