---
license: mit
task_categories:
- reinforcement-learning
- robotics
- time-series-forecasting
pretty_name: QCEA Adaptive Agent Benchmark
tags:
- econophysics
- multi-agent
- algorithmic-information-theory
- qcea
- universal-ai
- aixi
size_categories:
- <1K
---

# QCEA Adaptive Agent Benchmark: The Dancing Landscape
**Description:** The Dancing Landscape. A multi-regime dataset for stress-testing Universal Agents against the laws of Entropic Decay and Computational Irreducibility.
**Maintainer:** [Algoplexity](https://github.com/algoplexity)  
**Research Horizon:** Horizon 2 (Adaptive Strategy)

## 1. Overview
This repository contains the **Spatial-Causal State Vectors** required to train and validate the **AIT Physicist** in a multi-agent environment.

It serves as the "Petri Dish" for the **Horizon 2** research objective: **The Synthesis of QCEA and UAI.**
*   **The Environment (QCEA):** The data simulates a "Dancing Landscape" governed by **Quantum-Complex-Entropic** laws (Inertia vs. Interaction), creating a non-stationary challenge that breaks standard statistical models.
*   **The Target Agent (UAI):** This benchmark is specifically designed to stress-test agents built on **Universal Artificial Intelligence (AIXI)** principles, requiring them to perform *Algorithmic Compression* of the trajectory to survive, rather than memorizing a fixed policy.

## 2. Dataset Structure

### File: `h2_golden_benchmark.parquet`
A comprehensive temporal trace containing two partitions:
1.  **Natural World:** Traces captured from the live `birdgame` engine (representing the competitive reality).
2.  **Theoretical World:** Traces generated by the QCEA Physics Simulator (representing pure Rule 54/60 dynamics).

### Schema
*   **`source`** (string): Origin of data (`engine_native` or `qcea_synthetic`).
*   **`timestamp`** (int): Logical

## 4. Universal Loading (Python)

You can load this dataset directly into a Pandas DataFrame without manual downloading:

```python
from huggingface_hub import hf_hub_download
import pandas as pd

def load_landscape():
    repo_id = "algoplexity/qcea-adaptive-agent-benchmark"
    filename = "h2_golden_benchmark.parquet"
    
    print(f"--- Fetching The Dancing Landscape ---")
    path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset")
    return pd.read_parquet(path)

df = load_landscape()
```

## 5. Citation

```bibtex
@misc{qcea_benchmark_2025,
  author = {Mak, Yeu Wen},
  title = {QCEA Adaptive Agent Benchmark: The Dancing Landscape},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Dataset},
  howpublished = {\url{https://huggingface.co/datasets/algoplexity/qcea-adaptive-agent-benchmark}}
}
```