From 268c0461f94a3d11fc374be18f1b1653ff142fc1 Mon Sep 17 00:00:00 2001 From: cedric Date: Wed, 8 Apr 2026 17:56:26 +0000 Subject: [PATCH] feat(docs): expanded reward function docs --- docs/design/reward_function.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/design/reward_function.md b/docs/design/reward_function.md index 2237aa5..a72fb33 100644 --- a/docs/design/reward_function.md +++ b/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