Datasets:

algoplexity
/

qcea-adaptive-agent-benchmark

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
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:

Natural World: Traces captured from the live birdgame engine (representing the competitive reality).
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:

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

@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}}
}