MDP stands for Markov Decision Process, which is a mathematical framework used in the field of artificial intelligence and decision theory. It involves describing a decision-making problem as a sequential process in which an agent interacts with an environment. The MDP model is widely utilized in reinforcement learning algorithms.
In an MDP, the decision-making problem is formulated as a tuple (S, A, P, R), where:
- S represents a finite set of states, describing the possible conditions in which the agent can find itself.
- A is a finite set of actions available to the agent.
- P denotes the probability of transitioning to a new state when an action is taken in a particular state. It defines the dynamics of the environment.
- R represents the immediate reward the agent receives when it performs an action in a given state.
The goal of the agent is to devise an optimal policy, which is a mapping that determines the action to take in each state to maximize the cumulative expected future reward. This is achieved using dynamic programming algorithms like value iteration or policy iteration, or by utilizing other algorithms such as Q-learning or Monte Carlo methods.
MDPs are an essential framework for modeling decision-making problems in various domains such as robotics, game theory, and operations research. They provide a structured way to reason about the consequences of different decisions and enable intelligent systems to learn optimal behavior through interaction with the environment.
