Datasetpublic

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… See the full description on the dataset page: https://huggingface.co/datasets/felipedreis/dl2l-experiments.

sourceHugging Facemitupdated 1mo agoView on Hugging Face

0likes3.2kdownloads

Dataset Card

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

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