When: Jul 27th, 2021 – 11:00 – 11:45 AM
Where: Google meet link
In reinforcement learning, with the exception of some control problems, de-facto continuous action spaces are rare. They have a powerful feature though: they can be used to reduce the complexity of combinatorial discrete action spaces. Through a pratical example, I show how to approach this kind of problems and their drawbacks, more often then not deterimental to the overall learning capabilities of the model. Finally, a hierarchical approach is shown in place of the continuous one.