PPO Hyperparameters That Crash in Production: 5 Silent Failures

#ppo #hyperparameters #reinforcementlearnin #productionml

The 0.00001 That Killed Two Weeks of Training

You've babied your PPO agent through 10 million timesteps. The learning curve looks perfect. Validation rewards plateau at exactly where you need them. You deploy to production, and within 3 hours the policy collapses into a single repeated action.

I'm talking about hyperparameter configurations that pass every offline check but fail catastrophically when the environment shifts even slightly. Not the obvious failures — learning rate too high, network exploding. The silent ones. The bugs that don't throw errors, just quietly ruin your agent's decision-making until users notice the bot doing something profoundly stupid.

This post covers five PPO hyperparameter traps that only reveal themselves in production. Each one came from debugging deployed RL systems where the training metrics looked fine but the live behavior was unacceptable.