Self-Improving AI Agents Through Automated Skill Generation and Reinforcement Learning

This article examines how self-improving AI agents use automated skill generation and reinforcement learning to learn new skills, improve performance, and adapt to changing tasks. It explores key techniques, proposed architectures, challenges, and future research directions for developing intelligent, efficient, and autonomous AI systems.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-13
DOI
https://doi.org/10.5281/zenodo.23340560
Primary Topic
Reinforcement Learning in Robotics
Type
preprint
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preprint

Self-Improving AI Agents Through Automated Skill Generation and Reinforcement Learning

Smith Joe
Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
preprint

Self-Improving AI Agents Through Automated Skill Generation and Reinforcement Learning

Smith Joe
preprint en

Abstract

This article examines how self-improving AI agents use automated skill generation and reinforcement learning to learn new skills, improve performance, and adapt to changing tasks. It explores key techniques, proposed architectures, challenges, and future research directions for developing intelligent, efficient, and autonomous AI systems.

Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
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