Hi Laxmi,
The Reinforcement Learning toolbox provides the ‘rlNormalizer’ object which configure normalization for input of function approximator object. It ensures that the inputs are on a similar scale when provided to the network.
The ‘rlNormalizer’ has been introduced in MATLAB R2024a and later versions.
You can refer to the MATLAB Documentation of ‘rlNormalizer’ for better understanding:
You can also leverage the ‘unitsratio’ function for the conversion of input from one unit to another.
Refer to the example below:
mPerFoot = unitsratio("meter","feet");
MATLAB provides explicit functions ‘normalize’ and ‘zscore’ to scale the data. The ‘normalize’ function normalizes data within specified range whereas the ‘zscore’ depicts the standardized z-score.
Refer to the implementation below:
distance_normalized = normalize(distance, "range", [0, 1]);
distance_standardized = zscore(distance);
You can also leverage the MATLAB Documentation of ‘normalize’ and ‘zscore’ functions:
- ‘normalize’: https://www.mathworks.com/help/releases/R2024a/matlab/ref/double.normalize.html
- ‘zscore’: https://www.mathworks.com/help/releases/R2024a/stats/zscore.html
I hope this helps in getting started.
