ml-systems-engineer-rl-engineering

What problem does it solve?

This ML systems engineering guide for reinforcement learning provides a blueprint for building scalable, reliable RL training infrastructure—covering distributed training platforms, rollout workers, vectorized environments, replay buffers, policy/critic serving, checkpointing, experiment tracking, sim-to-real hooks, and overall training reliability.

Core Features & Use Cases

• Architecture and runbook design for RL training platforms (controllers, workers, resource scheduling)
• Environments and rollouts, replay buffers, and checkpointing for scalable experiments
• Exportable policy artifacts and reliable evaluation handoffs for downstream inference and validation
• Observability, reproducibility, and incident-driven reliability practices across RL pipelines

Quick Start

Describe your RL training setup and run a baseline on a small vectorized environment to validate the topology.