What is the benefit of a curiosity-driven curriculum?

It ensures the AI focuses on tasks that are neither too easy (already compressed) nor too hard (incompressible), maximizing the learning rate through an optimal curriculum.

Does this skill require specific compressors?

No, it is a general framework that can be applied using various compression algorithms or world models to calculate the intrinsic reward signal.

What is compression progress in AI?

Compression progress is the rate at which a learner's ability to compress data improves. In the context of AI, it serves as a formal measure of curiosity and learning.

How does the curiosity reward work?

The reward is proportional to the improvement in compression (old bits minus new bits). If the agent learns a new pattern, the reward is positive; if the data is already known or random, the reward is zero.

Compression Progress Curiosity Engine

Name: Compression Progress Curiosity Engine
Author: plurigrid

byplurigrid

0•

Data Science & ML

Implements Schmidhuber's compression progress theory to provide intrinsic curiosity rewards for autonomous AI learning and exploration.

The Compression Progress skill operationalizes the formal theory of curiosity by measuring the derivative of a model's compression ability over time. By calculating the difference in compressed data length between successive learning steps, it creates an intrinsic reward signal that motivates the agent to seek out novel, learnable patterns. This tool is essential for developers building autonomous agents, designing optimal learning curricula, and implementing exploratory AI behaviors that avoid both 'boredom' (already compressed data) and 'noise' (incompressible data).

Key Features

01Implements Curious Agent patterns for state exploration

02Visualizes learning trajectories through compression-trajectory logs

03Integrates formal mathematical frameworks including Cat# and GF(3) Triads

040 GitHub stars

05Calculates intrinsic reward based on compression improvement gradients

06Generates automated learning curricula based on task progress potential

Use Cases

01Creating self-improving systems that explore data based on intrinsic motivation

02Developing autonomous agents that prioritize high-learning-potential tasks

03Auditing machine learning models to identify plateauing learning progress

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi compression-progress

For use in Claude.ai and ChatGPT

Download Skill