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Explain a Markov Decision Process

VoleonMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenMachine Learning

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Review the components and trajectory semantics of an MDP, including state sufficiency, policies, transitions, rewards, and discounted return.

Explain a Markov Decision Process

Define a Markov decision process using states, actions, transition probabilities, rewards, and a policy. Explain the Markov property and how a trajectory produces return.

Constraints & Assumptions

  • Use a discrete formulation unless continuous spaces are explicitly requested.
  • State the discount or finite-horizon convention.
  • Reward may depend on state, action, and next state.

Clarifying Questions to Ask

  • What information must a state contain to be Markov?
  • Is the environment fully observed?
  • What objective is the policy optimizing?

Hints

Hint 1

Test the state: If the future still depends on omitted history after conditioning on the proposed state and action, the state representation is incomplete.

Follow-up questions

  • How does partial observability change the formulation?
  • What is the difference between episodic and continuing tasks?