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--- |
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language: |
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- en |
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task_categories: |
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- reinforcement-learning |
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- tabular-classification |
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- tabular-to-text |
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tags: |
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- human-demonstrations |
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- atari-like |
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- browser-game |
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- rl-dataset |
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- games |
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- gym |
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- reinforcement-learning |
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- robotics |
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- atari |
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- 2d |
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- RLHF |
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- RL |
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- video-game |
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license: mit |
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pretty_name: ARACHNID RL Dataset |
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size_categories: |
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- n<1K |
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config_name: default |
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--- |
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[](https://webxos.netlify.app) |
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[](https://github.com/webxos/webxos) |
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[](https://huggingface.co/webxos) |
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[](https://x.com/webxos) |
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<div style=" |
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background: #00FF00; |
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border-left: 4px solid #00FF00; |
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padding: 1.5rem; |
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margin: 2rem 0; |
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font-family: 'Fira Code', 'Courier New', monospace; |
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color: #00FF00; |
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border-radius: 0 8px 8px 0; |
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"> |
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<pre style=" |
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font-size: 8px; |
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line-height: 1.2; |
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margin: 0; |
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overflow-x: auto; |
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color: #00FF00; |
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"> |
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___ ___ ___ ___ ___ ___ |
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/\ \ /\ \ /\ \ /\__\ /\ \ /\ \ _____ |
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/::\ \ /::\ \ /::\ \ /:/ / \:\ \ \:\ \ ___ /::\ \ |
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/:/\:\ \ /:/\:\__\ /:/\:\ \ /:/ / \:\ \ \:\ \ /\__\ /:/\:\ \ |
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/:/ /::\ \ /:/ /:/ / /:/ /::\ \ /:/ / ___ ___ /::\ \ _____\:\ \ /:/__/ /:/ \:\__\ |
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/:/_/:/\:\__\ /:/_/:/__/___ /:/_/:/\:\__\ /:/__/ /\__\ /\ /:/\:\__\ /::::::::\__\ /::\ \ /:/__/ \:|__| |
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\:\/:/ \/__/ \:\/:::::/ / \:\/:/ \/__/ \:\ \ /:/ / \:\/:/ \/__/ \:\~~\~~\/__/ \/\:\ \__ \:\ \ /:/ / |
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\::/__/ \::/~~/~~~~ \::/__/ \:\ /:/ / \::/__/ \:\ \ ~~\:\/\__\ \:\ /:/ / |
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\:\ \ \:\~~\ \:\ \ \:\/:/ / \:\ \ \:\ \ \::/ / \:\/:/ / |
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\:\__\ \:\__\ \:\__\ \::/ / \:\__\ \:\__\ /:/ / \::/ / |
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\/__/ \/__/ \/__/ \/__/ \/__/ \/__/ \/__/ \/__/ |
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</div> |
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# ARACHNID RL Dataset |
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This dataset contains reinforcement learning transitions collected from human gameplay of ARACHNID RL, a 2D Atari-inspired space shooter. |
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It contains about 2,831 samples of human gameplay data from a simple Atari-inspired space shooter game. |
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Players control a spider-like ship to shoot asteroids and aliens while collecting diamonds. To build your own datasets |
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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 |
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features desktop keyboard and mobile oneclick browser support. |
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The dataset is designed for RL research, such as training agents via imitation learning or behavioral cloning from human demonstrations. |
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### Game Description |
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Players control a spider-like ship to destroy asteroids and aliens while collecting diamonds. Unlike large-scale RL datasets |
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focused on real-world robotics or massive web-derived preferences, arachnid_RL emphasizes simplicity and interpretability. |
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Its modest scale (1K–10K samples) makes it suitable for rapid prototyping, educational purposes, or benchmarking algorithms |
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on human-like decision-making in a dynamic but low-dimensional environment. |
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### Dataset Structure |
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The main dataset is in `data/train.jsonl` in JSON Lines format. 1.83 MB, stored primarily as a JSON Lines file (train.jsonl), |
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with an auto-converted Parquet version for efficient loading. |
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Each entry represents a single transition, including timestamp, session/player ID, event type (e.g., shoot, move, game_start, destroy_alien), action |
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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 |
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objects, etc.), next state, and event details. |
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### Data Format |
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Each line in `data/train.jsonl` is a JSON object with: |
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- `state`: Game state (JSON string containing position, velocity, lives, score, nearby objects) |
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- `action`: Player action (left, right, up, down, shoot, boost, none) |
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- `reward`: Immediate reward |
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- `next_state`: Next game state (JSON string) |
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- `done`: Episode termination flag |
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- `event_type`: Type of event |
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- `event_details`: Additional metadata (JSON string) |
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### Citation |
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```bibtex |
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@misc{arachnid_rl, |
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title = {ARACHNID RL Dataset}, |
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author = {WebXOS}, |
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year = {2026} |
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} |
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``` |
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### License |
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MIT License |
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© 2026 WebXOS |
