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In general, what are the advantages of RL with actor-critic methods over actor-only (or policy-based) methods? One practical benefit is that critics can use TD learning to bootstrap, allowing them to learn online on each step taken, plus learn in continuing problems. Pure actor algorithms like REINFORCE, cross-entropy method, and non-RL policy-only learners,...


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but I have been told that neural networks aren't made to predict values in that way, they really are best suited for classification into discrete classes I don't agree with this statement. I already trained many CNNs for regressions tasks where a continous output is trained and they generally perform very well. I think the general "advantage" for ...


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