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MLFlow for Reinforcement learning
1. Introduction
- What is MLflow?
MLflow is an open-source platform designed to help machine learning practitioners and teams manage the complexities of the ML lifecycle. It supports every stage of a machine learning project — from experimentation to deployment — making the process more organized, trackable, and reproducible.
- Why use MLflow for RL?
MLflow isn’t perfect for reinforcement learning (at least for now), but it’s still a widely used open-source tool for experiment tracking. By leveraging MLflow, we can gain several advantages:
Experiment Tracking: Log hyperparameters, metrics, and artifacts, and easily compare different training runs.
Model Management: Log and version RL models, making it easier to reload them later for evaluation or inference.
- However, RL comes with its own challenges:
It involves complex and irregular training loops based on episodes, timesteps, and environment dynamics.
MLflow isn’t designed for real-time decision tracking or live agent evaluation. Loading a model for inference is also more involved, as the agent and environment must be tightly coupled through shared definitions of state, action, and reward.
MLflow also doesn’t natively support distributed or multi-agent training workflows, unlike specialized tools like Ray Tune or RLlib.
