# Experiments

This directory contains the paper-facing experiment code, checkpoints, results, figures, GIFs, tables, and reports.

This directory contains two formal experiment categories:

- **A. Learned world models**: trainable image-input WMs evaluated on rollout prediction and WM-based planning.
- **B. Traditional non-WM controllers**: hand-designed control baselines evaluated on the same downstream tasks.

Main method:

- **FlowMo**: Flow-Momentum World Model, the proposed drift-aware world model for surface vehicles.

Category A learned WM comparisons:

- **LeWorldModel**: JEPA-style latent predictor under the shared clean-image protocol.
- **PlaNet RSSM**: recurrent state-space world-model baseline under the shared clean-image protocol.
- **TD-MPC2 Dynamics**: task-oriented latent dynamics baseline under the shared clean-image protocol.

Purpose of Category A: compare world-model architectures under identical image data, optimizer budget, parameter budget, rollout target, and evaluation protocol.

Category B traditional controllers:

- **PID/LOS controller**
- **Physics MPC No-Flow**
- **Current-Estimator MPC**
- **Oracle-Flow MPC**

Purpose of Category B: compare downstream task behavior against non-neural controllers that do not train a world model.

Baseline details are documented in `BASELINES.md`; the full experiment protocol is documented in `docs/EXPERIMENT_PROTOCOL.md`.

Design principles:

- Shared simulator, datasets, planning utilities, metrics, and visualization live in `shared/`.
- Each method has its own directory with `src/`, `checkpoint/`, and `result/`.
- Paper artifacts are collected in top-level `figures/`, `gifs/`, `tables/`, and `reports/`.
- Method names should be explicit and readable. Avoid cryptic suffixes in paper-facing file names.

Standard method interface:

```text
src/model.py      # build_model(), load_model()
src/train.py      # train(config)
src/predict.py    # rollout(model, batch)
src/config.py     # default_config()
```

Closed-loop planning for learned world models is implemented once in `evaluate_image_planning.py` so every learned method is evaluated through the same CEM interface.

Traditional controllers use:

```text
src/controller.py or src/mpc.py
src/evaluate.py
src/config.py
```

Formal clean-image configuration:

```text
image_size=160
visual_scale=2.5
train=data/paper/train.npz
test=data/paper/test_unseen_flow.npz and data/paper/test_unseen_boat_params.npz
```

Full paper-facing image pipeline:

```bash
python -m experiments.run_paper_image_pipeline
```

The default command runs the paper configuration end to end: train all learned world models, evaluate long rollout prediction, run FlowMo latent probes, evaluate closed-loop planning against traditional controllers, generate GIFs, and write the final report. Images are rendered online from simulator states, so no separate image-cache preparation step is required.

Manual image training:

```bash
python -m experiments.train_image_world_models
python -m experiments.evaluate_image_world_models
python -m experiments.evaluate_flowmo_latent_probes
python -m experiments.evaluate_image_planning --task reach_uniform --boat twin
python -m experiments.summarize_paper_image_results
```