Overview
Markov Decision Processes (MDPs) provide a structured way to model decision-making in uncertain environments. They consist of states, actions, rewards, and policies, which together help in understanding how to make optimal decisions. MDPs are widely used in various fields, including artificial intel...
Key Terms
Example: In a chess game, a state could represent the current arrangement of pieces on the board.
Example: In a game, moving a piece is an action.
Example: In a game, winning a round might yield a reward of +10 points.
Example: A policy could dictate that a player always moves to the nearest piece.
Example: The value function might estimate that being in a winning position has a high value.
Example: The Bellman equation helps in determining the optimal policy by evaluating future rewards.