Reinforcement Learning: Ornstein-Uhlenbeck noise #3499
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Signed-off-by: Tarek <[email protected]>
Signed-off-by: Tarek <[email protected]>
Signed-off-by: Tarek <[email protected]>
zoq
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Jun 27, 2023
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Thanks for putting this together.
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Description
This pull request implements the Ornstein-Uhlenbeck noise class, along with a unit test.
Implementation details
The Ornstein-Uhlenbeck process is a process that generates temporally correlated noise via a random walk with damping, which is commonly used in reinforcement learning algorithms.
OUNoiseclass provides areset()function that sets the internal state of the noise process to the specified mean (mu).sample()function to update the internal state based on the mean reversion rate (theta) and standard deviation (sigma), and returns the current state as a noise sample.How Has This Been Tested?
OUNoiseTestverifies the functionality of theOUNoiseclass by testing thereset()function and the generation of noise samples, ensuring that the sampled state has the expected size and is not equal to the reset state.