Class ExperimentReinforcementLearning<ROOT extends Agent,O,A,C>

public abstract class ExperimentReinforcementLearning<ROOT extends Agent,O,A,C>
extends ExperimentCustom
implements ReinforcementLearningModel<ROOT,O,A,C>, LearningAgentModelInterface<ROOT>

Base class for Reinforcement Learning experiments.

This class serves as an interface between the model and the platform (Learning Platform) using Reinforcement Learning to train some Learning Agent which is based on that platform (e.g. artificial neural network). The experiment itself is platform-agnostic and speaks in general terms. Experiment may be used in two forms:

• exported to the Learning Platform supported by AnyLogic,
• and used during simulation with an already trained Learning Agent (testing purpose) - together with the Learning Platform-specific connector block from the Reinforcement Learning Library.

The learning process depends on the specific Learning Platform.
Below, we describe an example use: it may perform several simulation runs - the sequence of so called Episodes. The number of Episodes is determined by the Learning Platform. Each Episode contains the following logic:
1. Get the Episode Configuration (used for model initialization) from the Learning Agent (may be absent for models which don't require specific initialization).
2. Create root model agent and initialize it using the Configuration: #applyConfiguration(Agent, Object)
3. Start simulation:
3.1 Check the Stop condition (if true, the Learning Platform will be notified about Episode termination condition)
3.2 Get Observation from the model: #getObservation(Agent, Object)
3.3 Pass the Observation to the Learning Agent and receive the Action (or alternative responses like Stop Episode or Stop learning)
3.4 Apply the Action to the model: #applyAction(Agent, Object)
3.5 Run the model until some Stepping point, which should be defined by a user event or another trigger inside the model. To define this point, call ExperimentReinforcementLearning.takeAction( this );, where this is an existing agent containing the event.
3.6 Repeat to 3.1

If your Learning Platform has finished learning, and you want to test your Learning Agent. To do that, add a block from platform-specific reinforcement learning library to your root (Main) model agent, fill in the properties of that block and ensure there is an event/code inside your model calling the code noted in the p.3.5 above. Then simply run any simulation experiment with your model.

Usage:

• Define observation, action and configuration data structure classes and corresponding methods (p.3).
• In order to define the moment when the reinforcement learning should take place, create an event or any other type action with the code described in p. 3.5 above.