docs: proper newlines formatting

docs/DEVELOPMENT.md

@@ -2,7 +2,7 @@
 
 This guide outlines how to set up the development environment for this project, prioritizing **reproducible builds**, **environment parity**, and **cross-hardware compatibility**.
 
-## Reproducibility &uv
+## Reproducibility & uv
 
 This project uses [uv](https://github.com/astral-sh/uv) to manage dependencies and virtual environments. The `uv.lock` file is the absolute source of truth for package versions and must always be committed.
 
@@ -48,6 +48,7 @@ The devcontainer provides an identical experience to local development but with
 ## Local Development (Alternative)
 
 If you prefer not to use Docker:
+
 1. Install [uv](https://docs.astral.sh/uv/getting-started/installation/).
 2. Run `uv sync --frozen` (CPU) or `uv sync --frozen --extra cuda` (GPU).
 
@@ -58,6 +59,7 @@ Verify your setup by running the JAX initialization test:
 ```bash
 uv run pytest tests/test_jax_init.py
 ```
+
 In the devcontainer, this will succeed on both CPU and GPU. A `GpuDevice` is expected if a GPU is detected and the `cuda` extra was installed.
 
 ## Logging & Monitoring

docs/HPC.md

@@ -33,6 +33,7 @@ qsub -l gpus=1 scripts/hpc/install.sh
 Our scripts are cluster-agnostic and do **not** have hardcoded GPU requirements. Instead, you must request GPUs at runtime using the `-l gpus=1` flag when submitting to a production GPU cluster.
 
 ### Debugging (Donphan)
+
 The `donphan` cluster does not support GPUs. Simply run the scripts without extra resource flags:
 ```bash
 module swap cluster/donphan
@@ -40,6 +41,7 @@ qsub scripts/hpc/train.pbs
 ```
 
 ### Production (Joltik, Accelgor, Litleo)
+
 These clusters provide GPU acceleration and **require** a GPU request at runtime:
 ```bash
 module swap cluster/joltik  # or accelgor/litleo
@@ -64,11 +66,13 @@ After installation, run these commands to ensure your environment is set up corr
    ```bash
    ls -d venvs 2>/dev/null && echo "FAIL" || echo ">>> PASS: Project root is clean."
    ```
+
 2. **Verify Library Versions (NumPy Fix)**:
    ```bash
    python -c "import numpy; print(f'NumPy: {numpy.__version__}')"
    # Expected: 2.x.x (Venv version), not 1.2x (System version)
    ```
+
 3. **Verify GPU Access**:
    ```bash
    python -c "import torch, jax; print(f'GPU: {torch.cuda.is_available()}'); print(f'JAX: {jax.devices()}')"

docs/api/analysis.md

@@ -5,6 +5,7 @@ This guide outlines the tools available for analyzing experimental data and gene
 ## Shared Configuration
 
 All plotting scripts share a central configuration in `scripts/plots/plot_config.py`. This file defines:
+
 - **Color Palette:** A color-blind friendly, high-contrast palette for different architectures.
 - **Typography:** Consistent font sizes and styles tailored for A0 posters.
 - **Markers:** Shared visual indicators, such as the ★ used for best performers.
@@ -40,6 +41,7 @@ uv run python scripts/plots/analyze_comparisons.py path/to/results.csv \
 ### Outputs
 
 The script generates four key plots, each saved as both `.png` and `.svg`:
+
 1.  **Forward Velocity:** Grouped bar chart (cm/s).
 2.  **Accumulated Reward:** Mean cumulative reward.
 3.  **Success Rate:** Target acquisition percentage.
@@ -65,6 +67,7 @@ uv run python scripts/plots/analyze_convergence.py --output_dir runs/convergence
 ### Outputs
 
 Generates three plots (PNG & SVG):
+
 1. `convergence_comparison`: Grouped horizontal bar chart.
 2. `progress_reward_curves`: Line plots of reward over time.
 3. `progress_velocity_curves`: Line plots of velocity over time.
@@ -74,7 +77,9 @@ Generates three plots (PNG & SVG):
 ## Poster Integration (Figma)
 
 ### SVG & Scaling
+
 We recommend using the **SVG** outputs for poster design in Figma:
+
 1. **No Resolution Loss:** SVGs are vector-based and will remain sharp at any size.
 2. **Native Text:** Text in the SVG imports as native text layers in Figma.
 3. **Exact Font Matching:** To ensure a `28pt` font in the plot matches a `28pt` font in your poster, set the `--fig_width` and `--fig_height` to match the physical dimensions of the plot box in your Figma layout.

docs/api/environment.md

@@ -1,12 +1,15 @@
 # Brittle star environment
 
 ## Creation
+
 The environment package contains a factory class `BrittleStarEnvFactory` 
 that creates instances of the environment/morphologies/... It uses the
 configuration classes defined in `env_config.py` to create the instances.
 
 ## Configuration
+
 The data classes in `env_config` have default values as stated in the tutorials.
+
 * MorphologyConfig: configuration for the morphology of the brittle star. Contains
 number of arms, number of segments per arm, and control mode.
 * ArenaConfig: configuration for the arena. Sets the size of the arena, whether to 
@@ -15,10 +18,13 @@ set the ground floor to sand, attach a target and sizes of the walls.
 such as camera locations, simulation time and the task.
 
 ## Backend and Task enums
+
 The Backend enum specifies either an MJC or MJX backend.
+
 * MJC: runs on CPU
 * MJX: uses jax on the gpu
 
 The Task enum specifies which task to use. 2 items are present:
+
 * DIRECTED_LOCOMOTION: move to a target location
 * LIGHT_ESCAPE: situation where the robot must move to a darker location

docs/design/communication.md

@@ -1,6 +1,7 @@
 # Communication scheme (Message Passing)
 
 Remember our research question:
+
 > "What is the impact of different levels of controller modularity on learning speed, coordination, and fault tolerance
 > (e.g. amputations) in brittle-star-like robots trained with Reinforcement Learning?"
 

docs/design/reward_function.md

@@ -8,8 +8,7 @@ inputs must be distributed fairly to guarantee an objective comparison between d
 - 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.
-- An extra penalty based on movement relative to the current step and
-the previous is used to penalize a movement away from the target.
+- An extra penalty based on movement relative to the current step and the previous is used to penalize a movement away from the target.
 
 ## From reward to PPO