kascecyber.blogg.se - Loopbe to martin mpc

#Loopbe to martin mpc how to#
#Loopbe to martin mpc archive#

Just delete this keyword if any set of parameters in the boundaries will work. You only need to set this if you have a safe set of parameters you want the experiment to start with. Here there are two value which each must be between -1 and 1.įirst_params defines the first parameters the learner will try. Num_params defines the number of parameters, min_boundary defines the minimum value each of the parameters can take and max_boundary defines the maximum value each parameter can take. Num_params = 2 #number of parameters min_boundary = #minimum boundary max_boundary = #maximum boundary first_params = #first parameters to try trust_region = 0.4 #maximum % move distance from best params In almost all cases you will only need to adjust the parameters settings and halting conditions, but we have also described a few of the most commonly used extra options. We will now explain the options in each of their groups.

#Loopbe to martin mpc archive#

#Tutorial Config #- #Interface settings interface_type = 'file' #Parameter settings num_params = 2 #number of parameters min_boundary = #minimum boundary max_boundary = #maximum boundary first_params = #first parameters to try trust_region = 0.4 #maximum % move distance from best params #Halting conditions max_num_runs = 1000 #maximum number of runs max_num_runs_without_better_params = 50 #maximum number of runs without finding better parameters target_cost = 0.01 #optimization halts when a cost below this target is found #Learner options cost_has_noise = True #whether the costs are corrupted by noise or not #Timing options no_delay = True #wait for learner to make generate new parameters or use training algorithms #File format options interface_file_type = 'txt' #file types of *exp_input.mat* and *exp_output.mat* controller_archive_file_type = 'mat' #file type of the controller archive learner_archive_file_type = 'pkl' #file type of the learner archive #Visualizations visualizations = True

#Loopbe to martin mpc how to#

In what follows we will unpack this process and give details on how to configure and run M-LOOP.

If using the neural net learner, then several neural_net_archive files will be saved which store the fitted neural nets.

learner_archive_.txt an archive of the model created by the machine learner of the experiment.

controller_archive_.txt an archive of all the experimental data recorded and the results.

M-LOOP_.log a log of the console output and other debugging information during the run.

M-LOOP also produces a set of plots that allow the user to visualize the optimization process and cost landscape.ĭuring operation and at the end M-LOOP writes these files to disk: Once the optimization process is complete, M-LOOP prints to the console the parameters and cost of the best run performed during the experiment, and a prediction of what the optimal parameters are (with the corresponding predicted cost and uncertainty). This process is repeated many times until a halting condition is met. The experiment should then write the file exp_output.txt which contains at least the variable cost which quantifies the performance of that experimental run, and optionally, the variables uncer (for uncertainty) and bad (if the run failed). The experiment is expected to run an experiment with these parameters and measure the resultant cost.

M-LOOP produces a file called exp_input.txt which contains a variable params with the next parameters to be run by the experiment. M-LOOP controls and optimizes the experiment by exchanging files written to disk. M-LOOP first looks for the configuration file exp_config.txt, which contains options like the number of parameters and their limits, in the folder in which it is executed.