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.

Inherited From

References