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Reinforcement learning (RL) utilizes policy gradient algorithms to directly optimize an agent's policy. These algorithms estimate the gradient of the expected reward relative to the policy's parameters.
This guide provides a practical explanation of the policy gradient theorem, its derivation, and a PyTorch implementation of the policy gradient algorithm.
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