# How to scale the action in a custom environment with DDPG?

I am trying to implement DDPG in a custom gym environment. The action is the relative allocation of funds between each asset. The action space is a Box with the shape of the available assets, with a minimum of 0 and a maximum of 1. Therefore, I need to make sure that the total sum of the actions $$a_1, a_2,...,a_N$$ equals one: $$\sum_{i=1}^N a_i= 1$$. So far, I have found two different approaches:

1. Braithwaite scales the actions inside the customized environment: https://github.com/acbraith/gym-asset_allocation

2. Fontura et al. scale the actions inside the DDPG algorithm: https://github.com/MLRG-CEFET-RJ/DRL-ALM (spinup -> algos -> pytorch -> ddpg> -> ddpg.py)

Which approach is recommended? And how can the different approaches affect the performance of the DDPG?