license: mit
language:
- en
tags:
- artificial-life
- reinforcement-learning
- world-model
- dl2l
task_categories:
- other
DL2L Experiments Dataset
Simulation trajectory data from the DL2L
distributed artificial life simulator, used to train JEPA world models.
See 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
{
"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:
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:
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