Advanced Reinforcement Learning Interview Questions #24 - The Amortization Trap

When you remove the Actor to “simplify” architecture, you quietly reintroduce per-step optimization and destroy the very latency guarantees control systems require.

You’re in a Senior AI Robotics interview at Google DeepMind. The interviewer sets a trap:

“We swapped our Actor-Critic stack for pure Q-learning to simplify our architecture. In our 14-DoF continuous action space, why does the standard argmax(Q) operation completely shatter our 5ms inference latency budget, and how do you fix it?”

90% of candidates walk r…