felipedreis/dl2l-experiments

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