Policy gradient is a type of reinforcement learning algorithm that optimizes the policy directly, adjusting the agent's actions based on the expected reward. This method contrasts with value-based approaches by focusing on learning a parameterized policy that can output actions without estimating the value of states or state-action pairs. By using policy gradients, agents can learn to make better decisions in complex environments, enabling more effective integration of artificial intelligence and machine learning.
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