is there a way to implement a custom preprocessing / featurizing routine into the training process?
Is such a feature already available?
I am currently making use of a featurizer to preprocess the observations from the environment.
As I haven't found a way to implement it into the agent, I had to define this preprocessor as a part of the environment.
Unfortunately, the preprocessor transforms the low-dimensional environment state into a high-dimensional feature vector,
which is then appended to the memory buffer.
Consequently, the training uses a huge amount of RAM, although it should be possible to perform the preprocessing just in time, directly after low-dimensional observations have been loaded from the memory.
Hello,
is there a way to implement a custom preprocessing / featurizing routine into the training process? Is such a feature already available?
I am currently making use of a featurizer to preprocess the observations from the environment. As I haven't found a way to implement it into the agent, I had to define this preprocessor as a part of the environment. Unfortunately, the preprocessor transforms the low-dimensional environment state into a high-dimensional feature vector, which is then appended to the memory buffer. Consequently, the training uses a huge amount of RAM, although it should be possible to perform the preprocessing just in time, directly after low-dimensional observations have been loaded from the memory.
Thank you.