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[D] Is Reinforcement Learning Practical?

Is reinforcement learning practical at this point for industry work? The most prominent examples we see are from DeepMind (AlphaStar, AlphaGo), but the team are world-class researchers (over 40 of them) who also worked closely with expert Starcraft 2 players with a ton of computing resources.

As someone who hasn’t had much experience in RL, I see potential applications but am unsure of the amount of work or practicality of it. For example, one potential application for RL is to learn fraudulent behavior in an online retailer system (i.e. Amazon, EBay) and proactively find methods of fraud before they happen. One could imagine all the unintended behavior of misspecified reward function being useful for finding exploits in a system ( https://openai.com/blog/faulty-reward-functions/). But there are a lot of issues to overcome, (some mentioned in this article https://www.alexirpan.com/2018/02/14/rl-hard.html) about sample inefficiency, not to mention having to build your own simulator (and hope it’s representative to some degree).

What are people’s opinion on the practicality of using RL in something like fraud? Does it even make sense to build a simple online retailer simulator? I ask because it while I think RL is quite powerful, it feels it isn’t quite ready to be used. I would love to be shown to be wrong.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.