Unit 5

RL Engineering

Best practices to ensure your RL results are measurable, reproducible, and comparable across seeds, environments, checkpoints, and evaluation protocols.

Chapter 1

Reinforcement Learning Engineering

Close the workbook by making RL results measurable, reproducible, and comparable across seeds, environments, checkpoints, and evaluation protocols.

1. Experiment Tracking→
2. Seeds and Reproducibility→
3. Evaluation Protocols→
4. Comparing Runs Fairly→
5. Environment and Reward Contracts→
6. Distributed RL and Deployment→
7. RL Engineering Checklist→