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Practical problems in artificial intelligence often involve both large state and/or action spaces where only partial information is available to the agent. In high-dimensional cases, function approximation methods, such as neural networks, are often used to overcome limitations of traditional tabular schemes. In the context of reinforcement learning, the actor-critic architecture has received much attention in recent years, in which an actor network maps states to actions and a critic produces value function approximation given a state action pair.
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