Policy Optimization

Policy optimization refers to a set of algorithms in reinforcement learning that aim to improve the decision-making policy of an agent. By adjusting the policy, the agent can maximize its expected cumulative rewards over time. Key characteristics of policy optimization include the use of gradient ascent methods to update policy parameters, and the ability to handle high-dimensional action spaces. Common use cases for policy optimization include training agents for games, robotics, and various decision-making tasks in uncertain environments.