Policy gradient methods are a type of reinforcement learning algorithm that optimize the policy directly rather than estimating the value function. These methods adjust the policy parameters to maximize the expected cumulative reward, making them suitable for complex decision-making tasks where actions need to be selected in a stochastic environment. They are particularly useful in training agents in environments with high-dimensional action spaces, as they allow for more flexible learning of policies compared to traditional value-based methods.
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