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

Wah Loon Keng, Laura Graesser, Milan Cvitkovic

Keywords: reinforcement learning, machine learning, benchmark, reproducibility, software, framework, implementation issues, parallelization, software platforms

TL;DR: We introduce a new software framework (SLM Lab) for reinforcement learning and use it to produce a massive performance benchmark of RL algorithms.

Abstract: 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.

Code: https://github.com/kengz/SLM-Lab

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1912.12482/code)