Create a Simulation
The first step to training a team of agents for a use case is to create or connect a simulation. A simulation is a virtual version of the real system where agents can make decisions, get feedback, and improve their performance over an infinite number of training episodes. This simulation is the training ground for the agents and gives them the feedback necessary to learn and improve.
Data-Driven Simulation
For material fusion, we will use AMESA's simulation lab to create a data-driven simulation. This means starting with a historical data set and then expanding that finite set of real data into an infinite set of representative synthetic data for agents to use for training.
There are a few basic steps to prepare data and create a simulation with AMESA:
Gather data
Merge, clean, and assess and remove outliers
Format for simulation
Upload the data to AMESA and receive a data score
Create simulation
Here is the prepared data set for material fusion.
1. Gather Data: Identify Variables
One of the most important steps in gathering data is to identify the variables that will be used in the dataset and ultimately the simulator.
Identify Core Variables
Not all variables in a dataset need to be included in a simulation. For example, an automated lane changing system for a self-driving car needs to know acceleration, lane position, and the position of other cars. But it does not need to know oil temperature, even if that variable is included in the full set of what is measured. The variables that are necessary to control the system are called core variables. Remove unnecessary variables and include only core variables in your dataset for AMESA.
There may also be variables in the dataset that are not directly measured, but are calculations based on variables that are directly measured. Do not include these in your dataset. If your agent needs access to this information, include the variables as perceptors.
Sensors and Actions
There are two types of variables to use with AMESA: sensors and actions.
Actions are control variables, “knobs” that are used to control the system
Sensors are environment or state variables, “gauges” that report on the conditions in the system
All variables are either sensors or actions.
For the Material Fusion use case, the variables are:
Action: temperature set point
Action: pressure set point
Sensor: material fusion metric - a measure of how well the materials have bonded
Sensor: material transformation metric - a measure of how well the separate plastics have physically transformed into a new substance
Quality rating: a measure of final product quality
2. Merge and Clean Data and Assess and Remove Outliers
Merge and Clean Data
Data for building simulations often lives in multiple systems, but AMESA requires a single data file to create a simulation. Merge the data into a single dataset.
AMESA can only accept data that is numeric. Remove any non-numeric data from the dataset.
AMESA will also ignore any row that has null values in it. Remove or fill null cells in the dataset. You may fill nulls with fill-forward, fill-backwards, or interpolation, depending on the specifics of the missing data.
Remove Outliers
Outliers are a common issue in industrial datasets, often caused by sensor malfunctions or other disturbances to the process. Outliers are also one of the most common reasons for a low simulation score in AMESA's simulation lab.
Outliers are tricky because they often represent simple bad data, in which case they should be removed from the dataset. But at times they represent legitimate instances of unusual behavior that the system needs to be able to learn to control. In those cases, they should remain in the dataset and be included in the simulated behavior used for agent training.
Use subject matter expertise to determine whether to remove outliers or include them on a case by case basis.
For the Material Fusion use case, the dataset is clean and does not need outliers removed.
3. Format the Data for Simulation
To create a simulation in AMESA:
The data should be saved in a .CSV file
The columns should be labeled as sensors and actions, with a s_ prefix before the variable name for sensors and an a_ prefix for actions (e.g. s_Material_Fusion_Metric, a_Temperature)
There should be no spaces or special characters in the variable names
Optionally, units can be included in the first line of the dataset
4. Upload the Dataset to Receive a Data Score
Upload the .CSV file to the AMEA platform. You will receive a data score that reflects how well the sensors and actions are correlated. Datasets with strong correlations can create high quality simulations.
5. Create Simulation
Click “create simulation.”
The platform will create the simulation and provide a simulation score. The simulation score measures how closely the synthetic data within the simulation matches the real data within the dataset.
You have now created a data-driven simulation and are ready to identify skills and build and train agents.
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