# Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation

arXiv: https://arxiv.org/abs/2201.03947 · HTML: https://mlsanity.com/paper/2201.03947/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 56 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [Organic Computing in the Spotlight](https://mlsanity.com/paper/1701.08125/) (2017)
- [Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning](https://mlsanity.com/paper/2008.03995/) (2020)

## Built on by (most cited first, 1 of 1)

- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)

## Abstract (arXiv)

Intelligent systems have the ability to improve their behaviour over time taking observations, experiences or explicit feedback into account. Traditional approaches separate the learning problem and make isolated use of techniques from different field of machine learning such as reinforcement learning, active learning, anomaly detection or transfer learning, for instance. In this context, the fundamental reinforcement learning approaches come with several drawbacks that hinder their application to real-world systems: trial-and-error, purely reactive behaviour or isolated problem handling. The idea of this article is to present a concept for alleviating these drawbacks by setting up a research agenda towards what we call "active reinforcement learning" in intelligent systems.