Policy gradient methods are a class of algorithms in reinforcement learning that optimize the policy directly by adjusting the parameters of the policy function based on the performance feedback received from the environment. These methods help to maximize the expected reward by using gradients to update the policy parameters, which allows for efficient learning in complex environments where traditional value-based approaches may struggle. They play a crucial role in integrating artificial intelligence and machine learning, especially in situations requiring continuous action spaces and complex decision-making processes.
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