ReinforcementLearning.jl

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First of all, I would like to say thank you to all of the contributors of this useful package!

I am a learner of both RL and this package. I wonder if RL or this package can deal with problems that have a continuous action space or a mixed integer action space. Specifically, suppose we have a decision making problem that at each step we are to make a decision (an action) $a$ according to current state $s$ and an observation of the random noise $\omega$, i.e., the action space $\mathcal{A}$ is characterized by some constraints such as $\mathcal{A} = { a| f(a, s, \omega) \le 0 }$ (BTW, why can't github display {}?). Can RL deal with this kind of problems? And how can I write such a environment using RenforcementLearning.jl?

Looking forward to your reply at your convenience! Thanks!