What is a Markov decision process in ML?

A Markov decision process (MDP) is a mathematical framework used in machine learning to model decision-making processes. It is named after the Russian mathematician Andrey Markov and is widely used in artificial intelligence for making optimal decisions in a stochastic environment.

Markov decision process

In an MDP, a decision is made at each step based on the current state of the system and the available actions. The key idea behind an MDP is the Markov property, which states that the future state of the system depends only on the current state and the action taken, not on the sequence of events that led to the current state.

Machine learning and decision-making processes

MDPs are used in machine learning to model a wide range of real-world decision-making problems, such as robotic navigation, resource management, and game playing. By defining the states, actions, rewards, and transition probabilities, an MDP can capture the dynamics of a complex system and help in finding the optimal policy that maximizes long-term rewards.

Optimal decisions in a stochastic environment

By solving the Bellman equation or using reinforcement learning algorithms such as Q-learning or policy iteration, an agent can learn to make optimal decisions in a stochastic environment. MDPs provide a systematic way to formalize decision-making problems and find strategies that lead to the best possible outcomes.