---
language:
- en
task_categories:
- reinforcement-learning
- tabular-classification
- tabular-to-text
tags:
- human-demonstrations
- atari-like
- browser-game
- rl-dataset
- games
- gym
- reinforcement-learning
- robotics
- atari
- 2d
- RLHF
- RL
- video-game
license: mit
pretty_name: ARACHNID RL Dataset
size_categories:
- n<1K
config_name: default
---

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# ARACHNID RL Dataset

This dataset contains reinforcement learning transitions collected from human gameplay of ARACHNID RL, a 2D Atari-inspired space shooter.
It contains about 2,831 samples of human gameplay data from a simple Atari-inspired space shooter game.

Players control a spider-like ship to shoot asteroids and aliens while collecting diamonds. To build your own datasets 
download the ARACHNID RL file in the /gym/ folder of this repo. Play the game and build your own datasets based off of input data. The game
features desktop keyboard and mobile oneclick browser support.

The dataset is designed for RL research, such as training agents via imitation learning or behavioral cloning from human demonstrations.

### Game Description

Players control a spider-like ship to destroy asteroids and aliens while collecting diamonds. Unlike large-scale RL datasets 
focused on real-world robotics or massive web-derived preferences, arachnid_RL emphasizes simplicity and interpretability. 
Its modest scale (1K–10K samples) makes it suitable for rapid prototyping, educational purposes, or benchmarking algorithms 
on human-like decision-making in a dynamic but low-dimensional environment.

### Dataset Structure

The main dataset is in `data/train.jsonl` in JSON Lines format. 1.83 MB, stored primarily as a JSON Lines file (train.jsonl), 
with an auto-converted Parquet version for efficient loading.

Each entry represents a single transition, including timestamp, session/player ID, event type (e.g., shoot, move, game_start, destroy_alien), action
taken (e.g., left, right, shoot), reward (e.g., +15 for collecting diamonds), done flag, current state (as JSON with position, velocity, score, lives, nearby 
objects, etc.), next state, and event details.

### Data Format

Each line in `data/train.jsonl` is a JSON object with:
- `state`: Game state (JSON string containing position, velocity, lives, score, nearby objects)
- `action`: Player action (left, right, up, down, shoot, boost, none)
- `reward`: Immediate reward
- `next_state`: Next game state (JSON string)
- `done`: Episode termination flag
- `event_type`: Type of event
- `event_details`: Additional metadata (JSON string)

### Citation

```bibtex
@misc{arachnid_rl,
  title = {ARACHNID RL Dataset},
  author = {WebXOS},
  year = {2026}
}
```

### License
MIT License

© 2026 WebXOS