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There are many algorithms that are not based on finding a value function. The most common ones are policy gradients. These methods attempt to map states to actions through a neural network. They learn the optimal policy directly, not through a value function. The important part of the image is when Model-Free RL splits into Policy Optimization (which ...


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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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