= Machine Learning Meeting Agenda, June 26, 5pm CST =

== 1) Cost function minimization ==
 * Cost function minimization algorithms, what has been done - simulations and experiment (Den)
 * NN based feedback algorithm (Masha)
 * Adaptive feedback LMS (Den)

== 2) Finding an optimal cost function ==
 * Creating an optimal cost function for a feedback control of a cavity, in what sense is it optimal? (Jenne)
 * Adaptation of a particular cost function to keep RMS of the cavity constant (Den)
 * How to convert lock loss minimization, actuator saturations, stability criteria and error minimization to cost function? (All)
 * MIMO loop tuning (Rana)

== 3) Sensor blending ==
 * Blending of GS13 and CPS, L4C and IPC for ISI and HEPI control in optimal way (Den)
 * [[https://intranet.aei.uni-hannover.de/geo600/geohflogbook.nsf?OpenDatabase | Pre-OMC heterodyne and DC-readout (-> p4344, 4376)]] blending or hierarchical control, is it useful? (Koji)

== 4) Reinforcement learning algorithms ==
 * Test of CARCA (Den)
 * Should we create a library of codes? (All)

== 5) Lock acquisition states ==
 * Correspondance to seismic motion, experience from i,eLIGO, Virgo, GEO, Tama... (Koji, Rana)
 * Seismic radar, k-maps, status of the project (Den)

== 6) Problems to solve ==
 * experiment - test CARCA online on an oplev (40m)
 * experiment - test seismic k-maps (LLO, 40m)
 * experiment, simulation - adaptive feedback LMS on OL (40m)
 * experiment - DRMI loop tuning (40m)
 * simulations - RL algorithm testing 
 * simulations - NN feedback controller
 * simulations - tuning IFO for a particular GW source - adjust control system, squeezing angle, readout type
 * simulations - loops crossover tuning - suspensions, frequency stabilization