Description: Q policy improvement is a fundamental process in reinforcement learning that focuses on optimizing an agent’s policy based on updated Q values. In this context, the policy refers to the strategy an agent follows to decide which action to take in a given state. Q policy improvement involves adjusting this strategy to maximize expected long-term rewards. This process is based on the idea that as the agent interacts with its environment and receives feedback in the form of rewards, it can refine its policy to be more effective. Q policy improvement is carried out using the Q-value function, which estimates the quality of an action in a specific state. Through successive iterations, the agent updates its Q-value estimates and consequently adjusts its policy to favor actions that have proven to be more beneficial. This approach allows the agent to learn from its experience, adapting to changes in the environment and improving its performance over time. Q policy improvement is essential for the development of artificial intelligence systems that require autonomous decision-making and is applied in various fields, from gaming to robotics and optimization problems.
