Policy gradient is a reinforcement learning technique used to optimize the decision-making process by directly adjusting the policy that defines the agent's behavior in an environment. This method works by calculating the gradient of the expected reward with respect to the policy parameters, allowing for more effective learning and adaptation over time. It is particularly useful in complex environments where traditional value-based methods may struggle to find optimal solutions, making it essential for applications in grid control and optimization.
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