RL_Interview_Qs Flashcards
(2.01) What is Reinforcement Learning?
(2.02) Can you explain the key components of a Reinforcement Learning problem: agent, environment, state, action, and reward?
(2.03) What is the difference between supervised learning, unsupervised learning, and reinforcement learning? supervised learning
(2.04) What are the two main types of reinforcement learning algorithms: model-based and model-free?
(2.05) What is the Markov Decision Porcess (MDP) and how is it related to Reinforcement Learning?
(2.06) Can you explain the concepts of exploration and exploitation in the context of RL?
(2.07) What is the difference between policy-based and value-based reinforcement learning methods?
(2.08) What is Q-Learning? How does it work?
(2.09) Can you describe the concept of discount factor (gamma) in RL and its purpose?
(2.10) What is the Bellman Equation? How is it used in reinforcement learning?
(2.11) What is the purpose of an epsilon-greedy strategy in RL?
(2.12) Can you briefly explain the Temporal Difference (TD) learning method?
(2.13) What are the main challenges of reinforcement leaarning?
(2.14) What are some popular of reinforcement learning in real-world scenarios?
(2.15) What is the role of the reward function in RL? Can you give an example?
(2.16) What is the Monte Carlo method in RL and when is it used?
(2.17) Can you explain the concept of state-value function (V) and action-value function (Q)?
(2.18) What is SARSA? How does it differ from Q-Learning?
(2.19) Can you provide an example of a continuous action space in RL?
(2.20) What is deep reinforcement learning? How does it combine deep learning and reinforcement learning?
(3.01) What are some common techniques to address the exploration-exploitation dilemma in RL?
(3.02) How does the concept of “credit assignment” apply to RL?
(3.03) What are the key differences between on-policy and off-policy learning in RL?
(3.04) Can you explain the concept of the “curse of dimensionality” in the context of RL and how it affects learning?
(3.05) What is the difference between asynchronous and synchronous Rl algorithms?
(3.06) Can you explain the idea of bootstrapping in Temporal Difference learning?
(3.07) What is the role of eligibility traces in reinforcement learning and how do they help with learning?
(3.08) Can you describe the concept of function approximation in RL and why it’s important?
(3.09) What is the REINFORCE algorithm and how does it relate to policy gradient methods?
(3.10) How does experience replay work in Deep Q-Networks (DQN) and why is it important?
(3.11) What is the role of target networks in DQN and how do they help stabilize learning?
(3.12) What is the difference between value iteration and policy iteration in dynamic programming applied to RL?
(3.13) Can you provide an example of a partially observable Markov decision process (POMDP) and how it differs from an MDP?
(3.14) What is the Proximal Policy Optimization (PPO) algorithm and why is it considered an improvement over traditional policy gradient methods?
(3.15) What are some common techniques to handle continuous action spaces in RL?
(3.16) Can you explain the concept of intrinsic motivation in RL?
(3.17) What is inverse reinforcement learning? How does it differ from regular reinforcement learning?
(3.18) can you describe the idea of hierarchical reinforcement learning and its potential benefits?
(3.19) How can transfer learning be applied to reinforcement learning and its potential benefits?
(3.20) What are the key differences between multi-agent reinforcement learning (MARL) and single-agent reinforcement learning?
(4.01) How do actor-critic methods combine the benefits of policy-based and value-based RL methods?
(4.02) What are the main challenges in scaling up reinforcement learning to real-world applications?
(4.03) How does Trust Region Policy Optimization (TRPO) improve the stability of policy updates in policy gradient methods?
(4.04) Can you explain the concept of Generalized Advantage Estimation (GAE) and its role in policy gradient methods?
(4.05) What are some advanced exploration strategies used in RL, beyond epsilon-greedy or softmax action selection?
(4.06) How do model-based RL algorithms utilize an internal model of the environment to improve learning?
(4.07) What is the role of meta-learning in RL and how can it improve learning efficiency?
(4.08) Can you explain the concept of multi-task RL and its potential advantages?
(4.09) What are the key principles of curriculum learning in RL and how can they be used to imporve agent training?
(4.10) How can Bayesian methods be incorporated into RL to improve exploration and learning?
(4.11) What is the role of information theory in RL? Specifically in terms of exploration and representation learning.
(4.12) Can you describe the idea of distributional RL and its potential benefits over traditional RL methods?
(4.13) What is Soft Actor-Critic (SAC) and how does it differ from other actor-critic algorithms?
(4.14) How do imitation learning and apprenticeship learning relate to RL?
(4.15) Can you explain the concept of off-policy correction in multi-agent RL?
(4.16) What are some popular benchmarks and environments used for evaluating and comparing reinforcement learning environments?
(4.17) How can the success of RL algorithms be measured beyond simple cumulative rewards?
(4.18) What are the key challenges in sample efficiency for RL algorithms and how can they be addressed?
(4.19) How can RL be applied to partially observable enviornments where the agent has incomplete information about the state?
(4.20) What are some ethical considerations and potential risks in deploying RL in real-world applications?
(5.01) Can you discuss the trade-offs between on-policy and off-policy algorithms in terms of sample efficiency and stability?
(5.02) How do importance sampling techniques help bridge the gap between off-policy and on-policy learning in RL?
(5.03) What is the role of entropy regularization in policy optimization and how does it affect exploration?
(5.04) How do online planning algorithms, like Monte Carlo Tree Search (MCTS), relate to and differ from RL methods?
(5.05) Can you discuss the challenges and strategies in designing reward functions for real-world RL applications?
(5.06) What are some techniques to address function approximation errors in value-based RL methods and how do they affect convergence?
(5.07) How can unsupervised and self-supervised learning methods be used to improve representation learning in RL?
(5.08) Can you explain the concept of goal-conditioned RL and its potential advantages in sparse reward settings?
(5.09) What are some practical challenges in training deep reinforcement learning models and how can they be addressed?
(5.10) How can safety constraints be incorporated into RL algorithms to ensure safe exploration and deployment?
(5.11) can you discuss the role of counterfactual reasoning in MARL and its potential benefits?
(5.12) How can RL be combined with symbolic reasoning to improve problem-solving capabilities?
(5.13) What are some key challenges in extending RL to deal with non-stationary environments?
(5.14) How do algorithms liek Twin Delayed Deep Deterministic Policy Gradients (TD3) address the overestimation bias of DDPG?
(5.15) What are some techniques for addressing the limitations of model-based RL, such as inaccurate environment models?
(5.16) How do continual learning methods help RL agents adapt to changing environments or tasks over time?
(5.17) Can you discuss some approaches to handling partial observability and information asymmetry in MARL?
(5.18) What is the role of adversarial training in RL? Specifically, in terms of robustness and exploration?
(5.19) How do algorithms like Hindsight Experience Replay (HER) help address the challenges of sparse rewards in RL?
(5.20) Can you discuss some recent advances in the intersection of NLP and RL?
(6.01) Can you discuss the connections between RL and optimal control theory and how they have influenced each other?
