Python Deep reinforcement learning for network attacks

Looking to build a custom environment for a DRL model that can classify a dataset into two categories: Attacks and Benign. I have created all the necessary code and data preprocessing. I am only missing the environment class which needs to take in a dataset and be able to feed the data into the DRL model.

Can you help me with Reinforcement learning?

I am trying to build a custom environment that can take a dataset and be able to classify the data into two categories.

I have preprocessed the dataset.

And have built classes for replay memory, agent, and deep neural network.

I am missing the custom environment class to be able to run the code.

https://github.com/mohamadkeaik/DRL_IDS_SDN

This is the link to the Github page.

DRL_main file contains the code of all the classes using the preprocessed dataset.

The "testing" file contains the function that I am testing to create the environment class.

Dataset link:
https://wetransfer.com/downloads/dac43a6f46dc6e9836ab0124bc17456420220418172848/e903219c33e1b8f5b76e536c843fef2320220418172848/672dd2

please note this dataset is preprocessed.

so it contains only 20columns instead of 80.

the preprocessing code is found in the Deep reinforcement learning file

Also, I think we would need to discuss how we are going to build the environment class as it is up to us to create its rules.

I need it by Friday.

thanks.