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Tagged with continuous-action-spaces double-dqn
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What Kind of Reinforcement Learning Algorithms Can Be Used when the Action Space is Unfeasibly Large?
I know Deep Q network as a $S\times A$ DNN which maps the $S$ dimensional statespace to q-values of $A$ distinct actions.
In my problem, the action space is still discrete, and finite, but depending ...
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