API Reference - Models - DeepDoubleExpectedStateActionRewardStateActionV2 (Double Deep Expected SARSA)

DeepDoubleExpectedStateActionRewardStateActionV2 is a neural network with reinforcement learning capabilities. It can predict any positive numbers of discrete values.

Constructors

new()

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

Parameters:

• 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. [Default 0.5]

• averagingRate: The higher the value, the faster the weights changes. The value must be set between 0 and 1. [Default: 0.995]

• 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

Inherited From

• ReinforcementLearningBaseModel

References

• Double Sarsa and Double Expected Sarsa with Shallow and Deep Learning

• SARSA Reinforcement Learning

• Reinforcement Learning with Neural Network