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

数据科学与机器学习

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

关于

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).

主要功能

Implements Curious Agent patterns for state exploration
Visualizes learning trajectories through compression-trajectory logs
Integrates formal mathematical frameworks including Cat# and GF(3) Triads
0 GitHub stars
Calculates intrinsic reward based on compression improvement gradients
Generates automated learning curricula based on task progress potential

使用场景

Creating self-improving systems that explore data based on intrinsic motivation
Developing autonomous agents that prioritize high-learning-potential tasks
Auditing machine learning models to identify plateauing learning progress

关于

主要功能

Implements Curious Agent patterns for state exploration
Visualizes learning trajectories through compression-trajectory logs
Integrates formal mathematical frameworks including Cat# and GF(3) Triads
0 GitHub stars
Calculates intrinsic reward based on compression improvement gradients
Generates automated learning curricula based on task progress potential

使用场景

Creating self-improving systems that explore data based on intrinsic motivation
Developing autonomous agents that prioritize high-learning-potential tasks
Auditing machine learning models to identify plateauing learning progress