Policy Optimization

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Policy optimization is a group of reinforcement learning methods that focus on improving the policy itself – the rule an agent uses to choose actions in each situation. The policy tells the agent how likely it is to pick certain actions, and policy optimization adjusts this decision rule so the agent earns higher rewards over time. Many approaches use gradients to gently update the policy so learning stays stable and doesn’t jump too far in one step.

Policy optimization differs from value-based methods because it updates behavior directly rather than relying on separate value estimates to choose actions. This makes it well-suited for tasks with continuous actions or complex decision spaces, such as robotics and advanced game environments. Algorithms like policy gradients and Proximal Policy Optimization are common choices because they balance learning speed with stability.

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