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If you have a good model, and can use model-based optimal control which has been understood for decades, then that is good but there's also not really a researc

If you have a good model, and can use model-based optimal control which has been understood for decades, then that is good but there's also not really a research problem? You can just do the simple, robust thing and it will work great. (i.e. "to publish more papers" is actually a legitimate reason if your job is explicitly to publish papers) You may enjoy the article, "A Tour of Reinforcement Learning: The View from Continuous Control". At least that researcher would agree that people doing RL don't pay enough attention to "classical" control. https://arxiv.org/abs/1806.09460 https://arxiv.org/abs/1806.09460

wenc 8y ago
> there's also not really a research problem? This is also my suspicion. :) But to ignore optimal control altogether makes me suspect many AI researchers aren't familiar with the body of research, and many who've managed a cursory read of Wikipedia may believe that the state of the art in optimal control are LQRs and LQGs, when it's really MPC (which can be thought of as a generalization of LQRs). Also, MPC is a model-type and optimization-algorithm agnostic paradigm, so there's plenty of ways to combine models/algorithms within its broad framework -- this is partly how many MPC researchers come up with new papers :). I think AI researchers should take a look at it in complement with RL for the problems they're trying to solve. Thanks for the link to the paper -- I will take a look.