> I hoped to learn a couple good RL algorithms and then never have to actually learn the concrete details of optimization I think that you can use optimization

> I hoped to learn a couple good RL algorithms and then never have to actually learn the concrete details of optimization I think that you can use optimization without having to learn anything about its algorithms (learning the basics is always advised of course, but that takes nowhere near as much effort). Nowadays, an off-the-shelf black-box solver will perform better than a custom implementation in most cases: what you lose in fine-tuning is more than compensated by the implementation quality and the sheer number of algorithms available. > best resources for branching out from RL to more classical control and optimization It depends on your problem. For general unstructured (nonconvex continuous) optimization, like the OP, you can use NLopt's documentation as a pragmatic starting point [1]. Optimal control is a whole different thing though, and you may have to start with some academic papers/books. I have been recommended this one [2], but I have not read it. [1] https://nlopt.readthedocs.io/en/latest/ https://nlopt.readthedocs.io/en/latest/ [2] https://link.springer.com/book/10.1007/978-1-4471-0967-9 https://link.springer.com/book/10.1007/978-1-4471-0967-9