ML Interview Questions

Explain the difference between on-policy and off-policy reinforcement learning methods. How do these approaches impact the learning process and what are some examples of algorithms that use each method?

What is the credit assignment problem in Reinforcement Learning, and what strategies can be employed to effectively address it?

Explain how Q-learning works, its theoretical foundations, and list some common limitations. Additionally, provide practical examples where Q-learning can be effectively applied.

Explain the Policy Gradient Theorem and describe how the REINFORCE algorithm implements this concept in Reinforcement Learning.

Compare model-based and model-free reinforcement learning approaches, focusing on their theoretical differences, practical applications, and the trade-offs involved in choosing one over the other.

Explain the Proximal Policy Optimization (PPO) algorithm and discuss why it is considered more stable compared to traditional policy gradient methods.

Explain how Monte Carlo Tree Search (MCTS) works and discuss its application in reinforcement learning, specifically in the context of algorithms like AlphaGo.

Explain the key innovations in Deep Q-Networks (DQN) that enhance the classical Q-learning algorithm for tackling complex environments.

Explain the explore-exploit dilemma in reinforcement learning and discuss how algorithms like ε-greedy address this challenge.