Overview
General Policy Iteration is a fundamental concept in reinforcement learning that focuses on the iterative process of evaluating and improving policies. By alternating between these two steps, agents can gradually refine their strategies to maximize rewards in various environments. Understanding this...
Key Terms
Example: A policy could dictate that a robot moves left when it sees an obstacle.
Example: The value function might indicate that being in state A is worth 10 points.
Example: An optimal policy for a game would lead to winning the game every time.
Example: MDPs are used to model situations like board games.
Example: In a maze, exploration might involve trying different paths.
Example: In a maze, exploitation would mean taking the path that has led to success before.