| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - artificial-life |
| - reinforcement-learning |
| - world-model |
| - dl2l |
| task_categories: |
| - other |
| --- |
| |
| # DL2L Experiments Dataset |
|
|
| Simulation trajectory data from the [DL2L](https://github.com/felipedreis/dl2l) |
| distributed artificial life simulator, used to train JEPA world models. |
| See [`felipedreis/dl2l-jepa`](https://huggingface.co/felipedreis/dl2l-jepa) for the trained models. |
|
|
| ## Dataset structure |
|
|
| Data is organized by experiment prefix. Each prefix contains parquet files for |
| model training and a `stats.json` with dataset metadata. |
|
|
| ``` |
| p9/ |
| train.parquet # single-encoder training set (trials 1–8) |
| val.parquet # single-encoder validation set (trials 9–10) |
| train_dual.parquet # dual-encoder training set (adds h_t homeostatic columns) |
| val_dual.parquet # dual-encoder validation set |
| stats.json # dims, feature order, normalisation stats, split sizes |
| ``` |
|
|
| ## p9 experiment |
|
|
| **Simulation:** 10 creatures × 10 trials, DL2L `basic.conf` world. |
| **Split:** trials 1–8 → train, trials 9–10 → val (trial-based to prevent cross-trial contamination). |
| **Sizes:** 359,782 train / 89,731 val samples. |
|
|
| ### Sample format |
|
|
| Each row is a `(s_t, a_t, emotion_target)` tuple: |
|
|
| | Column group | Columns | Description | |
| |---|---|---| |
| | Perception `s_t` | `distance`, `angle`, `direction` | Target object spatial features | |
| | Object type | `type_GRAY_APPLE`, `type_GREEN_APPLE`, `type_RED_APPLE`, `type_ROTTEN_APPLE`, `type_CACTUS`, `type_ALOE` | One-hot object type | |
| | Action `a_t` | `a_APPROACH`, `a_AVOID`, `a_EAT`, `a_ESCAPE`, `a_PLAY`, `a_SLEEP`, `a_TOUCH`, `a_TURN`, `a_WANDER` | One-hot chosen action | |
| | Target emotion | `final_hunger`, `final_sleep`, `final_apathy`, `final_stress`, `final_pain`, `final_tedium`, `final_fear`, `final_curiosity`, `final_fertility` | Absolute arousal after next regulation | |
|
|
| Dual-encoder parquet files additionally include: |
|
|
| | Column | Description | |
| |---|---| |
| | `ht_hunger`, `ht_sleep`, `ht_pain`, `ht_tedium` | Homeostatic state at action time (`h_t`) | |
|
|
| ### stats.json |
|
|
| ```json |
| { |
| "input_dim": 9, |
| "action_dim": 9, |
| "emotion_dim": 9, |
| "latent_dim": 64, |
| "internal_state_dim": 4, |
| "internal_latent_dim": 16, |
| "live_emotion_dims": [0, 1, 4, 5], |
| "perception_feature_order": ["distance", "angle", "direction", ...], |
| "action_index_order": ["APPROACH", "AVOID", "EAT", "ESCAPE", "PLAY", "SLEEP", "TOUCH", "TURN", "WANDER"], |
| "emotion_index_order": ["hunger", "sleep", "apathy", "stress", "pain", "tedium", "fear", "curiosity", "fertility"], |
| "feature_means": [...], |
| "feature_stds": [...], |
| "n_train": 359782, |
| "n_val": 89731 |
| } |
| ``` |
|
|
| ## Data extraction |
|
|
| Raw data is extracted from the PostgreSQL database with: |
|
|
| ```bash |
| python3 scripts/pg_extract.py --out /path/to/output --container <db-container> |
| ``` |
|
|
| This covers trajectories, sleep episodes, engrams, arousal history, behavioural |
| efficiency, perception coverage, traveled distances, consolidation batch stats, |
| and more. See `scripts/pg_extract.py` for the full list. |
|
|
| The ML training dataset is assembled from the CSV output with: |
|
|
| ```bash |
| cd ml |
| python3 -m scripts.prepare_dataset --wd /path/to/output --out data_p9 --dual |
| ``` |
|
|
| ## Citation |
|
|
| ``` |
| DL2L — Distributed Live to Learn, Learn to Live |
| Felipe Duarte dos Reis, CEFET-MG, 2017–2026 |
| https://github.com/felipedreis/dl2l |
| ``` |
|
|
