> For the complete documentation index, see [llms.txt](https://slm-lab.gitbook.io/slm-lab/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://slm-lab.gitbook.io/slm-lab/v4.2.0/development/modular-lab-components/algorithm-taxonomy.md).

# Algorithm Taxonomy

## :herb: Algorithm Taxonomy

Deep RL algorithms can be classified into a family tree based on their methods / functions they learn, such as the one shown below.

![Source: Foundations of Deep Reinforcement Learning, Graesser & Keng.](/files/-Lu8jgocbohDU-x6R16F)

Algorithms often extends an existing one by modifying or adding components. Most model-free algorithms are descended from SARSA and REINFORCE. The figure below shows some of the algorithms in SLM Lab, and their relationships.

![Source: Foundations of Deep Reinforcement Learning, Graesser & Keng.](/files/-Lu6cZI1Z0R9Qr16-yKB)

Naturally, implementations can be consistent with this theoretical taxonomy by using **class inheritance** and **modular components**. This is precisely what SLM Lab does.