ReinforcementLearning.jl
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Custom environment action and state space explanation
From my point of view, there should be a tutorial which implements an environment with a continuous action and state space.
It took me some time to find out, that I had to use IntervalSets.jl
instead of ClosedIntervals.jl
to define the continuous state spaces for the rand()
sampler.