Markov Decision Processes — AI Technical Interview Questions & Answers

Markov Decision Processes

Markov Decision Processes

  • MDP: states, actions, transition P(s′|s,a), reward R, discount γ. Policy π(s) → action. Value functions estimate expected return. RL learns policies when you don’t know P. Robot navigation and game AI use this language.
  • Output: idea / do / check for Markov Decision Processes. Fill those three in the viva.

Technical Interview Tip: Name the MDP tuple.

Code Examples for Markov Decision Processes

Example

# Markov Decision Processes
print("idea :", "Markov Decision Processes")
print("do   : one tiny example on paper")
print("check: one failure case")

Output: idea / do / check for Markov Decision Processes. Fill those three in the viva.

How to Explain Markov Decision Processes to an Interviewer

Explain Markov Decision Processes as if you are teaching a junior — definition, then one example.

S, A, P, R, γ. Policy + value.

What does the example print, and what does that prove?

idea / do / check for Markov Decision Processes

What mistake do freshers make with Markov Decision Processes?

Only the buzzword, no example