Markov Decision Process (MDP)

A Markov Decision Process (MDP) is a mathematical framework used to model decision-making situations where outcomes are partly random and partly under the control of a decision maker. An MDP provides a formalism for analyzing problems in reinforcement learning and sequential decision-making, comprising the following components:1. **States**: A set of possible states that represent all the situations that can occur. 2. **Actions**: A set of actions available to the decision maker in each state. 3. **Transition Model**: A probability distribution that describes the likelihood of moving from one state to another, given a specific action. This models the environment's dynamics. 4. **Rewards**: A reward function that assigns a numerical value to each state or state-action pair, indicating the immediate benefit received after transitioning to a new state. 5. **Policy**: A strategy that defines the actions to be taken in each state, which can be deterministic or stochastic.The goal in an MDP is typically to find an optimal policy that maximizes the expected cumulative reward over time. MDPs are widely applicable in various fields including economics, robotics, artificial intelligence, and operations research.