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#Anylogic get variable value update#
#Anylogic get variable value plus#
#Anylogic get variable value simulator#

#Anylogic get variable value update#

initialConfig1: update the name to match the initial configuration parameter.

initialPropN: update to match the actual names defined as

Update each of the lines flagged with %TODO comments as follows:.

'initialPropN', episodeConfig.initialConfig1) %TODO In = in.setVariable('initialProp1', 'initialProp1'. % must begin training your brain in the web, selecting the "Simulink Cartpole"īonsaiRunTraining(config, mdl, callback for running model with provided episode configurationįunction episodeStartCallback(mdl, episodeConfig) % Main entrypoint for training a Bonsai brain.

Create a new file called bonsaiTrain.m and add the following code:.

Relevant to your Bonsai workspace and existing simulator.

Update each of the lines flagged with %TODO comments with the details.

% path to csv file where episode data should be logged

#Anylogic get variable value simulator#

% % your simulator to advance, defaults to 60 % % time (in seconds) the simulator gateway should wait for % % set state and action schemas (overrides data from bonsaiBlock)Ĭonfig.stateSchema = %TODOĬnfigSchema = %TODO % path to bonsai block in your simulink model file, used to determine state and action schemasĬonfig.bonsaiBlock = "simulinkModelFileName/Bonsai" %TODO % simulator name, for an unmanaged simulator launched from the desktop to show up on the webĬonfig.name = "YOUR_SIMULATOR_NAME" %TODO Create a new file called bonsaiConfig.m and add the following code:Ĭonfig.workspace = "YOUR_WORKSPACE_ID" %TODOĬonfig.accessKey = "YOUR_ACCESS_KEY" %TODO.The Bonsai block at the same rate as your model. Leave DEFAULT_SIMULATION_RATE set to -1 (recommended) if you want to run.Create a new file called startup.m and add the following code:ĭEFAULT_SIMULATION_RATE = -1 % units: sec.Create a new file called SIM_PARAMS.m and define the variables required by.Variables defined in ModelObservationĪre populated at the start of each episode step by the getObservableįunction and map to the simulator states defined in your Inkling file. ModelObservation: must contain all the variables needed to define the.ModelAction values are providedīy the Bonsai brain to update the model at each step. ModelAction variables are accessible in theĮpisodeStep field of the Bonsai Connector object and map to the allowedīrain actions defined in your Inkling file. ModelAction: must contain all the variables the Bonsai brain passes back to.Map to the simulator configuration variables defined in your Inkling file. ModelConfig variables areĪccessible in the episodeStart field of the Bonsai Connector object and If the initialization configuration happens as part of the simulation ModelConfig: must contain all the variables needed to initialize a trainingĮpisode.Starting configuration, action space, and observation space of your model for The wrapper model includes three Java classes that are required to define the

Drag and drop the top-level agent on the project panel for yourĮxisting simulator model into the Wrapper agent.

In the editor panel of the wrapper agent, hover to the left to see the BonsaiĬonnector object and the getObservable function.

Open the wrapper agent type in the Wrapper model.

Open the Wrapper model and your existing simulator model in AnyLogic.

Browse to, and select, the BonsaiLibrary.jar file on your machine.

Select the Add button on right side of the dialog box.

#Anylogic get variable value plus#

Select plus sign (+) in the lower left corner to open the.

(BonsaiLibrary.jar) from the Bonsai AnyLogic GitHub repository. Wrapper model in the Dependencies section of the wrapper model properties

You can see the relationship between your existing model and the (the wrapper model) so the wrapper model can control the root agent of yourĮxisting model. Wrapping is the process ofĮmbedding your existing simulator model as a dependency in a new model Library before you can use it with Bonsai. You need to wrap your existing AnyLogic model and attach the Bonsai Connector If you do not haveĪn access key, follow the instructions in

You must have an access key set in your Bonsai workspace.

You must know your Bonsai workspace ID.

Validate your simulation for machine teaching andīuild a dedicated Python connector for Bonsaiįor more information about creating a local simulator.

You must have a local simulator implemented.You must have the Bonsai CLI installed.You must have the Microsoft Bonsai API for Python installed.If you already have Python installed and want to avoid collisions with anĮxisting installation, use a Python environment If you have never coded in Python before, we recommend completing the Introduction to Python Learn module before continuing. The instructions below assume you understand the basics of Python coding. You must be familiar with Python coding.