How does this skill help with Reinforcement Learning (RL)?

It provides a 'curiosity' signal that motivates RL agents to explore areas of an environment where they are likely to learn the most, rather than just wandering randomly.

What happens if the data is too complex or just noise?

If data is incompressible noise, the model cannot improve its compression, resulting in zero progress and a 'boredom' signal, causing the agent to move on to other tasks.

Does this skill require specific model architectures?

No, it is a general framework that can be applied to any system that can be modeled as a compressor or world model.

What is the core principle of the Compression Progress skill?

It is based on the principle that learning equals compression improvement. It measures the derivative of a model's ability to compress data to generate a reward signal.

Compression Progress Curiosity

Name: Compression Progress Curiosity
Author: plurigrid

byplurigrid

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Data Science & ML

Implements Schmidhuber's compression-based intrinsic reward to drive curiosity and exploration in learning systems.

The Compression Progress Skill provides a formal implementation of Schmidhuber's theory of curiosity, where learning is defined as the improvement in data compression over time. By calculating the derivative of compression ability, this skill generates intrinsic reward signals that allow AI agents to identify novel information, avoid repetitive or incompressible data (boredom), and navigate complex environments via an optimal learning curriculum. It is an essential tool for developers building autonomous agents and self-improving models that require motivation beyond external task-based rewards.

Key Features

017 GitHub stars

02Intrinsic reward calculation based on compression gradients

03Curiosity-driven exploration logic for autonomous agents

04Automated curriculum generation for optimal learning paths

05Visualization tools for model learning trajectories

06Formal integration with Kolmogorov complexity and GF(3) triads

Use Cases

01Generating 'interesting' synthetic data to maximize model learning efficiency

02Designing adaptive curricula for training complex machine learning models

03Developing self-supervised agents that explore environments autonomously

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