tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
Archived
1
Fork 0
Code Issues Releases Activity

fix: clarified seperate policy and value network part

Browse source
This commit is contained in:
cedric 2026-03-31 22:17:32 +00:00
parent 209f9b6a3d
commit 42e51e0653
1 changed files with 1 additions and 1 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/design_decisions/mlp_architecture.md
View file

@ -19,6 +19,6 @@ Inspired by: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-det
For starters we will execute our tests with simple models. Each MLP will have only 1 hidden layer. This will be expanded as needed. The exceptions are the input networks / feature extractors — they will be given 2 hidden layers and 64 nodes per layer as advised in the blog. This might change as we make progress in our experiments.
Our policy and value networks use separate networks as advised by the paper and the blog. For continuous actions this should allow better learning at a small cost.
Our policy and value networks use separate input networks / feature extractors as advised by the SEL3 course assistants and the blog. For continuous actions this should allow better learning at a small cost.
We use mean and log_std to represent the action distribution, because it is advised by previous research for learning stability and other reasons.