API Reference - Models - DeepClippedDoubleQLearning (Clipped Double Deep Q-Learning)
DeepClippedDoubleQLearning is a neural network with reinforcement learning capabilities. It can predict any positive numbers of discrete values.
It uses two neural networks where lowest maximum Q-values are selected for training.
Constructors
new()
Create new model object. If any of the arguments are nil, default argument values for that argument will be used.
DeepClippedDoubleQLearning.new(discountFactor: number): ModelObject
Parameters:
-
lambda: At 0, the model acts like the Temporal Difference algorithm. At 1, the model acts as Monte Carlo algorithm. Between 0 and 1, the model acts as both. [Default: 0]
-
discountFactor: The higher the value, the more likely it focuses on long-term outcomes. The value must be set between 0 and 1. [Default: 0.95]
Returns:
- ModelObject: The generated model object.
Functions
setModelParameters1()
Sets model parameters to be used by the model.
DeepClippedDoubleQLearning:setModelParameters1(ModelParameters1: ModelParameters)
Parameters:
- ModelParameters1: First model parameters to be used by the model.
setModelParameters2()
Sets model parameters to be used by the model.
DeepClippedDoubleQLearning:setModelParameters1(ModelParameters2: ModelParameters)
Parameters:
- ModelParameters2: Second model parameters to be used by the model.
getModelParameters1()
Sets model parameters to be used by the model.
DeepClippedDoubleQLearning:getModelParameters1(): ModelParameters
Returns:
- ModelParameters1: First model parameters that was used by the model.
getModelParameters2()
Sets model parameters to be used by the model.
DeepClippedDoubleQLearning:getModelParameters2(): ModelParameters
Returns:
- ModelParameters2: Second model parameters that was used by the model.