API Reference - Models - ProximalPolicyOptimization (PPO)
ProximalPolicyOptimization is a base class for reinforcement learning.
Notes
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The Actor and Critic models must be created separately. Then use setActorModel() and setCriticModel() to put it inside the AdvantageActorCritic model.
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Actor and Critic models must be a part of NeuralNetwork model. If you decide to use linear regression or logistic regression, then it must be constructed using NeuralNetwork model.
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Ensure the final layer of the Critic model has only one neuron. It is the default setting for all Critic models in research papers.
Constructors
new()
Create new model object. If any of the arguments are nil, default argument values for that argument will be used.
ProximalPolicyOptimization.new(discountFactor: number): ModelObject
Parameters:
- 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.
ProximalPolicyOptimization:setParameters(discountFactor: number)
Parameters:
- discountFactor: The higher the value, the more likely it focuses on long-term outcomes. The value must be set between 0 and 1.