RL in Production: Safe Exploration & Serving
Log propensities, cap the blast radius, and serve a learned policy without betting the business on it
Learn the systems discipline that separates a reinforcement learning paper from a reinforcement learning product: why you must log the probability your policy assigned to the action taken (not just the action) so later off-policy evaluation is possible, how to enforce hard guardrails a learned policy may never violate, how to bound exploration traffic and duration, the end-to-end serving architecture from feature fetch to action logging, and how a deployed policy shapes the very data it will be retrained on.
Practice questions (5)
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Diagnose a Missing Propensity in Production Logs
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Reward Penalty vs. Structural Guardrail for a Hard Notification Cap
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Size an Exploration Budget for a New Bandit Arm
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Design the Request Path for a Latency-Constrained Policy Service
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Detect and Fix a Closing Feedback Loop
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