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Markov Decision Process (MDP) is a mathematical framework used in reinforcement learning to model decisionmaking problems. In other words, reinforcement learning often uses the framework of MDPs to formalize and solve decisionmaking problems. An MDP is defined by a five tuple (S, A, P, R) (note that the tuple here refers to the five components that define the MDP, which is different from the concept of a tuple in programming, where it is often used to represent an ordered collection of elements):

State Space (S): A set of all possible states the agent can be in. States represent the situation or configuration of the environment.

Action Space (A): A set of all possible actions the agent can take. Actions are the decisions or moves that the agent can make.
 Transition Probability Function (P): This function defines the probability of transitioning from one state to another given a particular action. It is often represented as P(s, a, s'), which is the probability of transitioning from state s to state s' by taking action a. The sum of probabilities over all possible next states (s') given a current state (s) and an action (a) is often expressed as follows,
 [3687a]
where,
 P(s's, a) is the probability of transitioning to state s' given that the current state is s and the agent takes action a.
 The sum over all possible s' represents the certainty that the next state will be one of the possible states.
 Reward Function (R): This function defines the immediate reward the agent receives after taking a particular action in a specific state. It is often represented as R(s, a, s'), which is the reward obtained when transitioning from state s to state s' by taking action a.
The MDP framework assumes the Markov property, which states that the future state depends only on the current state and action, not on the sequence of states and actions that preceded them. In other words, the system has no memory of past events beyond the current state.
Markov Decision Processes (MDPs) are a fundamental framework in reinforcement learning. Some notable examples where MDPs are applied are:

Grid World Navigation (see page3683):
 Scenario: Grid world is a classic example where an agent navigates through a grid of cells, encountering various states, and receiving rewards or penalties based on its actions.
 Application: The agent's goal is to find the optimal policy (π) for moving from a start state to a goal state while maximizing the cumulative reward. MDPs help model the state transitions, actions, and rewards, making it a suitable application for reinforcement learning.
 Robotics and Autonomous Vehicles:
 Scenario: Autonomous systems, such as robots or selfdriving cars, often use MDPs to make decisions in dynamic environments.
 Application: The state space could represent the current position, velocity, and orientation of the robot or vehicle, and actions might include moving in different directions or adjusting speed. The reward function could reflect reaching a destination, avoiding obstacles, or minimizing energy consumption. MDPs enable these systems to learn optimal policies for navigation.
 Inventory Management:
 Scenario: Businesses often face inventory management challenges, where decisions must be made to balance stock levels, order quantities, and costs.
 Application: MDPs can model the decisionmaking process for when and how much inventory to reorder based on the current stock levels, demand forecasts, and ordering costs. The goal is to find a policy that minimizes costs while ensuring that products are available when needed.
 Game Playing:
 Scenario: Games provide a rich environment for reinforcement learning, and MDPs can be used to model the decisionmaking process of an agent playing a game.
 Application: In games like chess or Go, the states represent the current board position, actions correspond to possible moves, and rewards reflect the outcome of the game. By learning from experience, an agent can develop strategies to maximize its chances of winning.
 Healthcare Treatment Planning:
 Scenario: Personalized treatment planning in healthcare involves making decisions about the best sequence of treatments for a patient over time.
 Application: MDPs can be used to model the patient's health states, treatment options, and the effects of different interventions. The goal is to find a policy that maximizes the patient's longterm health outcomes while considering factors like side effects and treatment costs.
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