DeepDoubleExpectedStateActionRewardStateActionV2 is a neural network with reinforcement learning capabilities. It can predict any positive numbers of discrete values.
Create new model object. If any of the arguments are nil, default argument values for that argument will be used.
DeepDoubleExpectedStateActionRewardStateAction.new(epsilon: number, averagingRate: number, discountFactor: number): ModelObject
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.
averagingRate: The higher the value, the faster the weights changes. The value must be set between 0 and 1.
discountFactor: The higher the value, the more likely it focuses on long-term outcomes. The value must be set between 0 and 1.
Set model’s parameters. When any of the arguments are nil, previous argument values for that argument will be used.
DeepDoubleExpectedStateActionRewardStateAction:setParameters(epsilon: number, averagingRate: number, discountFactor: number)
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.
averagingRate: The higher the value, the faster the weights changes. The value must be set between 0 and 1.
discountFactor: The higher the value, the more likely it focuses on long-term outcomes. The value must be set between 0 and 1.