| | --- |
| | 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}} |
| | } |
| | ``` |
| |
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