[andreyk]

All that stuff is in part two! https://thegradient.pub/how-to-fix-rl/

Says as much at the end... to be fair we did warn up front "The first part, which you're reading right now, will set up what RL is and why it is fundamentally flawed. It will contain some explanation that can be skipped by AI practitioners." But personally I think the board game allegory is fun and that most people tend to forget the categorical simplicity of Go and Atari games and overhype ; easy to say the main points are not new but the details are important here.

[jonnycomputer]

calling model-free RL "fundamentally flawed" is just click-baiting. too bad it worked on me; but I was hoping for insight.

[ddoolin]

In your opinion, is this a solution to the "AI winter" that is often talked about? I'm an engineer but not involved in AI but things like meta-reinforcement seem, from the info/perspective you've given, to address the problem, at least partially.

[andreyk]

I think AI winter is unlikely to come about this time since non-RL stuff (supervised learning) has been so successful and useful.

[Iv]

Yes, some techs are overhyped (chatbots, finance stuff) but deeplearning has delivered a lot of incredible working applications. It is not just hot air or marketing hype.

[backpropaganda]

Expert systems were not just hot air or marketing hype. Usefulness of a subset of new AI technology is irrelevant. A winter or contraction is caused by expectations not being met, and it seems, at least to me, that investors/funders have already started expecting superhuman performance in image/speech recognition, and there's a lot of expectation even in robotics, which will probably not be met by actual results any time soon.