Is There Any Challenge With Interpretability of Reinforcement Learning?

Sally Kohn: One of the things that we hear a lot about when we're working with partners on data science problems. And I wondered if you could talk a little bit about any challenges you see with interpretability of reinforcement learning, kind of the final results that are coming back. She says it can be quite easy to explain the result of an algorithm for more conceptual reasons like strategy or business decisions. But then there's already inherent problems such as complex neural network-based model being used in depths of your RL algorithm.

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