💡Motivation

Why SLM Lab Exists

Deep RL has many moving parts: algorithms, environments, neural networks, hyperparameters. Without proper tooling, it's easy to lose track of what works and why.

SLM Lab was built to bring the workflow of experimental science to deep RL:

1. Hypothesis - "What if we increase the learning rate?"

2. Experiment - Configure via JSON spec, run on server

3. Analysis - Automated metrics and graphs

4. Recording - Results stored with full reproducibility

The Problem It Solves

Pain Point	SLM Lab Solution
Managing command-line arguments	JSON spec files
Manually tracking hyperparameters	Automatic logging and versioning
Comparing results across runs	Hierarchical analysis (session → trial → experiment)
Reproducing others' results	Spec file + git SHA = exact reproduction
Debugging training failures	Comprehensive metrics and checkpointing

Design Principles

Modularity

Components are designed for reuse:

• The same network can work with any algorithm

• Memory systems are interchangeable

• New algorithms inherit most functionality

Simplicity

Code structure mirrors how algorithms are explained in papers and textbooks. If you understand the theory, the code is readable.

Analytical Clarity

Results should be interpretable:

• Experiment graphs show which hyperparameters work

• Trial graphs show consistency across seeds

• Session graphs show learning dynamics

Reproducibility

Every experiment can be exactly reproduced:

• Spec files capture all configuration

• Git SHA pins the code version

• Random seeds are recorded

• Results are stored on HuggingFace

Who It's For

Researchers: Quickly test hypotheses with rigorous evaluation Practitioners: Find working configurations for new environments Students: Learn algorithms through modular, readable implementations Educators: Teach RL with a complete, working framework

What's in the Name

SLM stands for Strange Loop Machine, named after Douglas Hofstadter's Gödel, Escher, Bach. The book explores self-reference and emergence in intelligence—themes that resonate with RL's goal of building agents that learn from experience.