# SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1912.12482 · HTML: https://mlsanity.com/paper/1912.12482/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization](https://mlsanity.com/paper/2305.05760/) (2023)
- [The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2103.05737/) (2021)

## Abstract (arXiv)

We introduce SLM Lab, a software framework for reproducible reinforcement learning (RL) research. SLM Lab implements a number of popular RL algorithms, provides synchronous and asynchronous parallel experiment execution, hyperparameter search, and result analysis. RL algorithms in SLM Lab are implemented in a modular way such that differences in algorithm performance can be confidently ascribed to differences between algorithms, not between implementations. In this work we present the design choices behind SLM Lab and use it to produce a comprehensive single-codebase RL algorithm benchmark. In addition, as a consequence of SLM Lab's modular design, we introduce and evaluate a discrete-action variant of the Soft Actor-Critic algorithm (Haarnoja et al., 2018) and a hybrid synchronous/asynchronous training method for RL agents.