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.
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