Your question is Batch Normalization and RL Optimization. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
What is batch normalization, and what are ways to optimize a reinforcement learning algorithm?
Explain batch normalization mathematically, including training and inference behavior, then discuss when it helps or harms neural reinforcement learning. Propose and implement a practical optimization strategy covering stability, sample efficiency, exploration, and computational cost. Include concrete evaluation metrics and justify tradeoffs between batch normalization, layer normalization, target networks, replay strategies, and gradient-based tuning.