# Tag Info

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I think the comments are basically on the right track. PID controllers are useful for finding optimal policies in continuous dynamical systems, and often these domains are also used as benchmarks for RL, precisely because there is an easily derived optimal policy. However, in practice, you'd obviously prefer a PID controller for any domain in which you can ...

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There's no difference. As they too often do, ML researchers take concepts from other disciplines, conveniently forget to cite sources and change the terminology, leading to much confusion. RL is a textbook example (pun intended). Optimal control researchers have been studying very similar problems long before RL ones, and used standard symbols and terms ($x$ ...

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As a supplement to nbro's nice answer, I think a major difference between RL and optimal control lies in the motivation behind the problem you're solving. As has been pointed out by comments and answers here (as well as the OP), the line between RL and optimal control can be quite blurry. Consider the Linear-Quadratic-Gaussian (LQG) algorithm, which is ...

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The same book Reinforcement learning: an introduction (2nd edition, 2018) by Sutton and Barto has a section, 1.7 Early History of Reinforcement Learning, that describes what optimal control is and how it is related to reinforcement learning. I will quote the most relevant part to answer your question, but you should read all that section to have a full ...

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Assumption In this answer it is assumed that with "neurochips" you mean chips made (using neuromorphic engineering) for neuromphic computing. Related example From what I currently understand from this article neuromorphic chips, in particular the TrueNorth chip, are being used (or emulated) for embedded systems related signals processing. Doubt The ...

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Extracting a joint PDF just means that you create a model that models the behavior of several variables combined instead of in isolation. If these variables aren't independent and your loss functions is influenced by all of them, you obviously have to learn this joint PDF to minimize your loss. So I don't see this statement as particularly mysterious.

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In general, you can use a simulation to prepare and train a controller for a real world application. A good example of this being done for robotics is in the paper Autonomous helicopter flight via reinforcement learning where a Reinforcement Learning agent was trained on a model of helicopter dynamics before being used in reality. Often, as in this case, ...

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I have tried to use DQN in Keras, but I am not sure that I am using correct state variables/reward. You have a wide range of choices that are all valid. As it is a simple control and learning scenario, provided you cover basics (described in a moment), then the difference in your choices are about how easy you make it for the agent to learn. You may ...

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