DeepDoubleStateActionRewardStateActionV1 is a neural network with reinforcement learning capabilities. It can predict any positive numbers of discrete values.
It uses Hasselt et al. (2010) version, where a single neural network is selected from two neural networks with equal probability for training.
Create new model object. If any of the arguments are nil, default argument values for that argument will be used.
DeepDoubleStateActionRewardStateAction.new(discountFactor: number): ModelObject
Set model’s parameters. When any of the arguments are nil, previous argument values for that argument will be used.
DeepDoubleStateActionRewardStateAction:setParameters(discountFactor: number)
Sets model parameters to be used by the model.
DeepDoubleStateActionRewardStateAction:setModelParameters1(ModelParameters1: ModelParameters, doNotDeepCopy: boolean)
ModelParameters1: First model parameters to be used by the model.
doNotDeepCopy: Set whether or not to deep copy the model parameters.
Sets model parameters to be used by the model.
DeepDoubleStateActionRewardStateAction:setModelParameters2(ModelParameters2: ModelParameters, doNotDeepCopy: boolean)
ModelParameters2: Second model parameters to be used by the model.
doNotDeepCopy: Set whether or not to deep copy the model parameters.
Sets model parameters to be used by the model.
DeepDoubleStateActionRewardStateAction:getModelParameters1(doNotDeepCopy: boolean): ModelParameters
Sets model parameters to be used by the model.
DeepDoubleStateActionRewardStateAction:getModelParameters2(): ModelParameters