feat(docs): expanded reward function docs

docs/design/reward_function.md

@@ -9,6 +9,10 @@ inputs must be distributed fairly to guarantee an objective comparison between d
   within a finite number of timesteps $T$.
 - To motivate efficient movement, the amount of timesteps taken to reach the goal will be used as penalty.
 
+## From reward to PPO
+
+The resulting reward is passed to our PPO library. Our critic network (value function) predicts how good our eventual reward will be for the current state, this value is combined with the reward from the reward function to get advantages. These advantages are then used to calculate the losses to update both our critic and actor pipeline.
+
 ## Rationale
 
 Using a light source (or a gradient) is biologically plausible for many simple organisms. By normalizing all signals