Why does the effectiveness of my reinforcement based neural network recede after a while? I have a reinforcement based neural network training on the OpenAI gym CartPole-v1 environment. For the structure and training algorithm, assume it is the same as the one in this article.

Typically, it averages becoming more effective, eventually perfectly solving the environment (500/500 reward for several hundred games in a row) but then starts to regress. Typically somewhere in the range of 100~150/500 total reward. This seems to happen regardless of learning rate, but I haven't tested with very low learning rates (less than 0.01) because of the amount of time it takes to train.

Can anyone tell me why this happens? I can't seem to find any literature on it, but perhaps I just don't know the name.
There seems to be some kind of overfitting happening.It happens when noise and fluctuation in data is learned as concept by your Neural Network Model.

https://en.wikipedia.org/wiki/Overfitting