diff --git a/docs/README.md b/docs/README.md
index ac56e31..a4f4ea0 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -2,11 +2,11 @@
 
 ## Design & architecture ([`/design`](./design/))
 
+- [Actor/critic architecture](./design/actor-critic.md): Description of the actor-critic pipeline.
 - [Communication](./design/communication.md): Message propagation, Nerve-Net style.
 - [Controllers](./design/controllers.md): Macroscopig brain toplogy, centralized, arm-level, segment-level.
 - [Input/output](./design/input_action_spaces.md): Description of the model's input and output.
 - [Learning algorithm](./design/learning_algorithm.md): RL techniques, i.e. PPO.
-- [MLP architecture](./design/mlp_architecture.md): Module wiring, state/action spaces, and actor-critic pipeline.
 - [Reward function](./design/learning_algorithm.md): Goals, fitness tracking, and reward structures.
 
 ## API reference ([`/api`](./api/))
diff --git a/docs/design/actor-critic.md b/docs/design/actor-critic.md
index e01ea96..245fefd 100644
--- a/docs/design/actor-critic.md
+++ b/docs/design/actor-critic.md
@@ -20,7 +20,6 @@ This pipeline treats the agent as a single entity and uses standard Proximal Pol
 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.
 
 ```mermaid
-%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%%
 graph TD
     Obs([Global Observation])
 
@@ -69,7 +68,6 @@ critic for all nodes at once, for the following reasons:
   mathematically equivalent.
 
 ```mermaid
-%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%%
 graph TD
     Obs([Local Observation])
 
@@ -96,7 +94,6 @@ graph TD
     Crit --> OutCrit
 
     Prop -.->|"message passing"|Prop
-
 ```
 
 ## Implementation Details (Network Depth)