I am using DDPG to solve a RL problem. The action space is given by the Cartesian product $[0,20]^4\times[0,6]^4$. The
actor is implemented as a deep neural network with an output dimension equals to $8$ with
So, given a state
s, an action is given by
a = actor(s) where
a contains real numbers in
[-1,1]. Next, I map this action
a into a valid action
valid_a that belongs to the action space $[0,20]^4\times[0,6]^4$. Than, I use
valid_a to calculate the reward.
My question is: how does the DDPG algorithm know about this mapping that I am doing? In what part of the DDPG algorithm should I specify this mapping? Should I provide a bijective mapping to guarantee that the DDPG algorithm learns bad from good actions?