docs: restructure docs for clarity

docs/README.md

@@ -8,7 +8,7 @@ For the core codebase, scripts, and contribution history, visit our [GitHub Repo
 
 - **[Installation Instructions](./DEVELOPMENT.md)**: Steps to set up your development environment locally or in a devcontainer using `uv`, including GPU configuration. For High-Performance Computing (HPC) setup details, see the **[HPC Guide](./HPC.md)**.
 - **[How to Run Experiments](./api/training.md)**: A complete guide on running training jobs, setting custom hyperparameters, and overriding config options using Hydra.
-- **[Results & Reproduction](./reproduction.md)**: Guide on how to access our public WandB training runs table and reproduce our training and evaluation phases (determining the best checkpoint vs. comparing architectures).
+- **[Results & Reproduction](./api/reproduction.md)**: Guide on how to access our public WandB training runs table and reproduce our training and evaluation phases (determining the best checkpoint vs. comparing architectures).
 - **[Repository Structure](#repository-structure)**: Overview of the directories and files within the codebase.
 
 ## Repository Structure
@@ -31,20 +31,21 @@ For the core codebase, scripts, and contribution history, visit our [GitHub Repo
 
 If you are interested in the "why did you do it like this?"
 
-- [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.
-- [Reward function](./design/reward_function.md): Goals, fitness tracking, and reward structures.
+- [Actor-Critic Architecture](./design/actor-critic.md): Description of the actor-critic pipeline.
+- [Communication Scheme](./design/communication.md): Message propagation, Nerve-Net style.
+- [Modularity & Topology](./design/controllers.md): Macroscopic brain topology, centralized, arm-level, segment-level.
+- [Input & Action Spaces](./design/input_action_spaces.md): Description of the model's input and output.
+- [Reinforcement Learning Algorithm](./design/learning_algorithm.md): RL techniques, i.e. PPO.
+- [Reward Function & Observation Space](./design/reward_function.md): Goals, fitness tracking, and reward structures.
 
 ## API reference (`/api`)
 
 If you are interested in the "how do I use it?"
 
-- [Training](./api/training.md): How to configure and run experiments.
+- [Brittle Star Environment](./api/environment.md): MuJoCo environment interaction and configuration.
+- [Training Models](./api/training.md): How to configure and run experiments.
 - [Tracking & Monitoring](./api/tracking.md): Setting up WandB and TensorBoard to monitor runs.
-- [Simulation](./api/simulation.md): Visualizing and evaluating models.
-- [Environment](./api/environment.md): MuJoCo environment interaction and configuration.
-- [Analysis](./api/analysis.md): Comparing checkpoints and generating plots.
-- [Evaluation](./api/evaluation.md): Evaluating checkpoints and comparing fault tolerance.
+- [Checkpoint & Model Evaluation](./api/evaluation.md): Evaluating checkpoints and comparing fault tolerance.
+- [Interactive Simulation & Visualization](./api/simulation.md): Visualizing models in the MuJoCo viewer or rendering simulation videos.
+- [Analysis & Plotting Tools](./api/analysis.md): Comparing checkpoints and generating plots.
+- [Results & Reproduction](./api/reproduction.md): Accessing WandB results and running reproduction pipelines.