The Pathmind Helper is an AnyLogic palette item that enables you to apply reinforcement learning to your simulations. It serves two purposes:
- Exposes simulation data and converts it into a format that a reinforcement learning algorithm can consume.
- Enables you to query a trained policy in AnyLogic.
Step 1: Download the Pathmind Helper (259 MB).
Step 2: Add the PathmindHelper.jar* as an AnyLogic palette item.
*[IMPORTANT] The PathmindHelper zip file contains two JAR files. These two files must always be in the same directory at all times. However, only PathmindHelper.jar needs to be added to AnyLogic as a palette item.
Step 3: Drag and drop the Pathmind Helper palette item into your top-level agent (typically "Main", which is the AnyLogic default)*.
*[IMPORTANT] There can only be a single instance of type Pathmind Helper per AnyLogic model. Adding more than one will result in an error.
Pathmind Helper Properties
Click on the Pathmind Helper to view its properties.
Enable or disable the Pathmind Helper. This option is commonly used to disable Pathmind Helper when switching between a heuristic and Pathmind.
A toggle to print simulation data passed to Pathmind Helper. This is helpful for auditing purposes.
Use Random Actions tells Pathmind to select actions at random. This option serves two purposes:
- Confirm that your action space is configured correctly. If your agent does nothing, there is likely a logic error in your simulation.
- Construct a baseline to measure the performance of a trained policy. Better than random is generally a good starting point.
Use Policy will execute your simulation using the trained policy obtained from Pathmind instead of random actions. When
Use Policy is selected, the Pathmind Helper will query the policy to predict the next best action.
Reinforcement Learning Values
Number of Controlled Agents is defined as the total number of controlled agents in your simulation.
Observations should contain anything that is relevant for an agent to make an informed decision.
Metrics are the building blocks for the reward function. Metrics can embody important simulation metrics such as revenue and cost. These metrics are likely what you directly seek to optimize.
Actions are a list of all possible actions that an agent can perform.
The event trigger tells an agent when it should execute an action. The event trigger must return
true (execute action) or
false (do not execute action).