How to choose inputs to maximize reward for an ordered dataset?
It seems like a reinforcement learning question with continuous action space. (ob1, ob2) is the observation, (x, y) the action, and reward the reward.

You may refer to this paper from DeepMind, which provides a general deep Q-learning way solving this type of question, including some physics tasks.

However, as I don't have your data, I'm not sure if you have sufficient instances. ~70,000 instances with 4 features is (roughly) sufficient for general ML problems, but in reinforcement learning world, people use a simulation environment which is able to generate infinite samples, thus ~70,000 instances may be the bottleneck.

If you encounter such problem, you may try classic reinforcement learning (without "deep") techniques.