API Reference - Models - DeepExpectedStateActionRewardStateAction (Deep Expected SARSA)
DeepExpectedStateActionRewardStateAction is a neural network with reinforcement learning capabilities. It can predict any positive numbers of discrete values.
Constructors
new()
Create new model object. If any of the arguments are nil, default argument values for that argument will be used.
DeepExpectedStateActionRewardStateAction.new(epsilon: number, discountFactor: number): ModelObject
Parameters:
-
maxNumberOfIterations: How many times should the model needed to be trained.
-
epsilon: Controls the balance between exploration and exploitation for calculating expected Q-values. The value must be set between 0 and 1. The value 0 focuses on exploitation only and 1 focuses on exploration only.
-
discountFactor: The higher the value, the more likely it focuses on long-term outcomes. The value must be set between 0 and 1.
Returns:
- ModelObject: The generated model object.
Functions
setParameters()
Set model’s parameters. When any of the arguments are nil, previous argument values for that argument will be used.
DeepExpectedStateActionRewardStateAction:setParameters(epsilon: number, discountFactor: number)
Parameters:
-
epsilon: Controls the balance between exploration and exploitation for calculating expected Q-values. The value must be set between 0 and 1. The value 0 focuses on exploitation only and 1 focuses on exploration only.
-
discountFactor: The higher the value, the more likely it focuses on long-term outcomes. The value must be set between 0 and 1.