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Temporal difference (TD) learning, a key concept in reinforcement learning, is a popular method for solving simulated control problems. However, in real systems, this method is often avoided in favor of policy search methods because of its long learning time. But policy search suffers from its own drawbacks, such as the necessity of informed policy parameterization and initialization. In this paper,...
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