From f487d5725fd1d286a4a910eb323dd60b10dfbc5c Mon Sep 17 00:00:00 2001 From: cedric Date: Tue, 31 Mar 2026 22:20:19 +0000 Subject: [PATCH] feat: extended reward function doc with efficiency idea --- docs/design/reward_function.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/design/reward_function.md b/docs/design/reward_function.md index 1bf8725..2237aa5 100644 --- a/docs/design/reward_function.md +++ b/docs/design/reward_function.md @@ -7,6 +7,7 @@ inputs must be distributed fairly to guarantee an objective comparison between d - Positions and joints, which are normalized to floating-point values between 0 and 1, are considered local inputs. - The reward function is centered around minimizing the distance to the goal or maximizing the movement towards the goal 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. ## Rationale