create either a file logger object or a monitor logger object to log training data -凯发k8网页登录

this example shows how to log data to disk when using train.

create a filelogger object using rldatalogger.

logger = rldatalogger();

specify a directory to save logged data.

logger.loggingoptions.loggingdirectory = "mydatalog";

create callback functions to log the data (for this example, see the helper function section), and specify the appropriate callback functions in the logger object.

logger.episodefinishedfcn    = @myepisodefinishedfcn;
logger.agentstepfinishedfcn  = @myagentstepfinishedfcn;
logger.agentlearnfinishedfcn = @myagentlearnfinishedfcn;

to train the agent, you can now call train, passing logger as an argument such as in the following command.

trainresult = train(agent, env, trainopts, logger=logger);

while the training progresses, data will be logged to the specified directory, according to the rule specified in the filenamerule property of logger.loggingoptions.

logger.loggingoptions.filenamerule

ans = 
"loggeddata"

function datatolog = myepisodefinishedfcn(data)
    datatolog.experience = data.experience;
end

function datatolog = myagentstepfinishedfcn(data)
    datatolog.state = getstate(getexplorationpolicy(data.agent));
end

function datatolog = myagentlearnfinishedfcn(data)
    datatolog.actorloss  = data.actorloss;
    datatolog.criticloss = data.criticloss;
end

this example shows how to log and visualize data to the window of a trainingprogressmonitor object when using train.

create a trainingprogressmonitor object. creating the object also opens a window associated with the object.

monitor = trainingprogressmonitor();

create a monitorlogger object using rldatalogger.

logger = rldatalogger(monitor);

create callback functions to log the data (for this example, see the helper function section), and specify the appropriate callback functions in the logger object. for the specific function signatures and more information on the function input structure, see the corresponding properties of monitorlogger.

logger.agentlearnfinishedfcn = @myagentlearnfinishedfcn;

to train the agent, you can now call train, passing logger as an argument such as in the following command.

trainresult = train(agent, env, trainopts, logger=logger);

while the training progresses, data will be logged to the training monitor object, and visualized in the associated window.

note that only scalar data can be logged with a monitor logger object.

function datatolog = myagentlearnfinishedfcn(data)
    if mod(data.agentlearncount, 2) == 0
        datatolog.actorloss  = data.actorloss;
        datatolog.criticloss = data.criticloss;
    else
        datatolog = [];
    end
    
end

this example shows how to log data to disk when training an agent using a custom training loop.

create a filelogger object using rldatalogger.

flgr = rldatalogger();

set up the logger object. this operation initializes the object performing setup tasks such as, for example, creating the directory to save the data files.

setup(flgr);

within a custom training loop, you can now store data to the logger object memory and write data to file.

for this example, store random numbers to the file logger object, grouping them in the variables context1 and context2. when you issue a write command, a mat-file corresponding to an iteration and containing both variables is saved with the name specified in flgr.loggingoptions.filenamerule, in the folder specified by flgr.loggingoptions.loggingdirectory.

for iter = 1:10
    % store three random numbers in memory 
    % as elements of the variable "context1"
    for ct = 1:3
        store(flgr, "context1", rand, iter);
    end
    % store a random number in memory 
    % as the variable "context2"
    store(flgr, "context2", rand, iter);
    % write data to file every 4 iterations
    if mod(iter,4)==0
        write(flgr);
    end
end

clean up the logger object. this operation performs clean up tasks like for example writing to file any data still in memory.

cleanup(flgr);