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Top 55 Reinforcement Learning Interview Questions
Entry Junior Mid Senior Expert
Reinforcement Learning Theoretical Questions
Q1:
What is Reinforcement Learning? How does it compare with other ML techniques?
Entry
Q2:
What is the difference between Off-Policy and On-Policy Learning?
Junior
Q3:
What is Markov Decision Process?
Junior
Q4:
Provide an intuitive explanation of what is a Policy in Reinforcement learning
Junior
Q5:
What are the steps involved in a typical Reinforcement Learning algorithm?
Junior
Q6:
What is the role of the Discount Factor in Reinforcement Learning?
Junior
Q7:
Name some approaches or algorithms you know in to solve a problem in Reinforcement Learning
Junior
Q8:
What does a Stationary Dynamics and Stationary Policy mean in the context of Reinforcement Learning?
Junior
Junior
Q10:
What is the difference between a Reward and a Value for a given State?
Junior
Junior
Junior
Q13:
Related To: Q-Learning
Can Q-learning be used for continuous (state or action) spaces? If not, then what would you use?
Related To: Q-Learning
Mid Q14:
Why would you use a Policy-based method instead o a Value-based method?
Mid Q15:
Why would you use a Deep Q-Network?
Mid Mid Q17:
Are there any problems when using REINFORCE to obtain the optimal policy?
Mid Q18:
How to choose the values of Gamma and Lambda in generalised temporal differencing algorithms?
Mid Q19:
Related To: Q-Learning
What's the difference between a Deep Q-Network and a categorical Deep Q-Network?
Related To: Q-Learning Mid Q20:
Are there any problems when using the Epsilon-Greedy method to find the Optimal Policy?
Mid Q21:
What's the difference between Learning Rate Decay and Epsilon Decay? What is the context of each one?
Mid Q22:
What's the advantage of using Policy Iteration vs Value iteration?
Mid Mid Q24:
What's the difference between a Deterministic vs Stochastic policy?
Mid Q25:
How does the Q function differ from the Value function in Reinforcement Learning?
Mid Q26:
Related To: Q-Learning
What is the difference between Q-Learning and SARSA and when would you use each one?
Related To: Q-Learning Mid Q27:
Can you think of an example of an Epsilon-Greedy Policy in real life?
Mid Q28:
What types of Reinforcement Learning Environments do you know?
Mid Mid Mid Mid Q32:
How to distinguish Episodic Tasks vs Continuous Tasks?
Mid Q33:
Related To: Monte Carlo Method
How does the Monte Carlo prediction method compute the Value Function?
Related To: Monte Carlo Method Mid Mid Q35:
Related To: Monte Carlo Method
Name some advantages of using Temporal difference vs Monte Carlo methods for Reinforcement Learning
Related To: Monte Carlo Method Mid Q36:
Related To: Monte Carlo Method
Name some advantages of using Monte Carlo vs Dynamic Programming methods in Reinforcement Learning
Related To: Monte Carlo Method Mid Q37:
Related To: Q-Learning
What are some advantages of Quantile Regression DQN over Categorical DQN?
Related To: Q-Learning Senior Q38:
What is the effect of Parallel Environments in Reinforcement Learning?
Senior Senior Q40:
Are there any problems when using a Softmax Function to select actions in a Deep Q-Network?
Senior Q42:
How does a Double Deep Q-Network differ from a Deep Q-Network?
Senior Q43:
Is the optimal policy always Stochastic if the environment is also Stochastic?
Senior Senior Q45:
How can policy gradients be applied in the case of multiple continuous actions?
Senior Q46:
What are some best practices when trying to design a Reward Function?
Senior Q47:
What is the difference between vanilla policy gradient (VPG) with a baseline as value function and advantage actor-critic (A2C)?
Senior Q48:
What is Sample Efficiency, and how can Importance Sampling be used to achieve it?
Senior Q49:
Related To: Q-Learning
What's the difference between Deep Q-Learning and Policy Gradient Method?
Related To: Q-Learning Senior Q50:
Can you apply Value Iteration and Policy Iteration in any environment?
Senior Q51:
Why do regular Q-Learning and DQN overestimate the Q values?
Expert Q52:
Related To: Q-Learning
What's the difference between Advantage Actor-Critic (A2C) and Asynchronous Advantage Actor-Critic (A3C)?
Related To: Q-LearningA2C) and Asynchronous Advantage Actor-Critic (A3C)? Expert Q53:
What is Experience Replay and what are its benefits?
Expert Q54:
How does the Actor-Critic method differ from the Policy Gradient with the Baseline method?
Expert Q55:
Can SARSA be used in a Partially Observable Markov Decision Process? If yes (or not), why?
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