Reinforcement Learning Interview Questions

Common RL interview patterns

• Markov Decision Process formulation
• Value iteration and policy iteration
• Q-learning and Deep Q-Networks (DQN)
• Policy gradient methods (REINFORCE, PPO)
• Exploration vs exploitation strategies (epsilon-greedy, UCB)
• Reward shaping and sparse reward challenges

Common mistakes in RL interviews

• Confusing value functions with policy functions
• Not discussing the sample efficiency problem
• Ignoring reward hacking and misaligned objectives
• Over-simplifying the exploration-exploitation dilemma
• Not considering the sim-to-real transfer gap

How RL questions are evaluated

Related ML concepts

• Deep learning interview questions
• Model evaluation interview questions