Using continuous action spaces to solve discrete problems

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Learning Action Representations for Reinforcement Learning (2019, background): “To deal with discrete actions that might have an underlying continuous representation, Van Hasselt and Wiering [2009] used policy gradients with continuous actions and selected the nearest discrete action.”
Reinforcement Learning in Continuous State and Action Spaces (2012, methodology): “In this section we discuss the continuous actor-critic learning-automaton (Cacla) algorithm (van Hasselt and Wiering, 2007, 2009).”