Reinforcement Learning MCQs January 8, 2026August 9, 2024 by u930973931_answers 26 min Score: 0 Attempted: 0/26 Subscribe 1. What is the main objective of reinforcement learning (RL)? (A) To classify data into categories (B) To learn an optimal policy to maximize cumulative reward (C) To predict future values based on past data (D) To find correlations between variables 2. In RL, what is an “agent”? (A) The entity that makes decisions and learns from interaction with the environment (B) The environment in which the agent operates (C) The rewards received from the environment (D) The state of the environment 3. What does the term “policy” refer to in reinforcement learning? (A) A mapping from states to actions (B) A set of actions that can be taken in an environment (C) The rewards given by the environment (D) The process of updating the Q-values 4. Which of the following is a common approach to solving RL problems? (A) Supervised Learning (B) Unsupervised Learning (C) Clustering (D) Q-Learning 5. What is the “reward” in reinforcement learning? (A) The action taken by the agent (B) The value of the state in which the agent finds itself (C) A measure of how well the agent performs in the environment (D) The policy used by the agent 6. In RL, what does the “value function” represent? (A) The immediate reward received after taking an action (B) The expected return or cumulative reward of being in a state (C) The mapping from states to actions (D) The probability distribution over actions 7. What is the “Q-function” in Q-Learning? (A) A function that estimates the value of a state (B) A function that maps states to actions (C) A function that represents the policy of the agent (D) A function that represents the expected reward for a state-action pair 8. Which of the following is an off-policy algorithm? (A) SARSA (B) Policy Gradient (C) Q-Learning (D) Actor-Critic 9. In the context of RL, what does “exploration” mean? (A) Exploiting the current knowledge to maximize rewards (B) Updating the value function based on rewards (C) Trying new actions to discover their effects and improve the policy (D) Selecting the action with the highest Q-value 10. What is “exploitation” in reinforcement learning? (A) Selecting the action that maximizes the expected reward based on current knowledge (B) Using random actions to discover new strategies (C) Updating the policy based on exploration (D) Learning the value function from experience 11. What does the “Bellman Equation” describe? (A) The optimal policy for a given environment (B) The probability distribution over actions (C) The relationship between the value of a state and the values of its successor states (D) The reward function of the environment 12. Which algorithm uses a model of the environment to predict future states and rewards? (A) Model-Free Methods (B) Policy Gradient Methods (C) Value Iteration (D) Model-Based Methods 13. In RL, what does “Temporal Difference (TD) Learning” refer to? (A) Learning by exploiting the current policy (B) Learning by using a complete trajectory of states and rewards (C) Learning by updating the value function based on immediate rewards (D) Learning by comparing the difference between successive predictions 14. What is the “discount factor” in reinforcement learning? (A) A parameter that determines the importance of future rewards (B) A measure of the immediate reward received by the agent (C) The probability of taking a specific action (D) The value function of the agent 15. What is “Policy Gradient” in reinforcement learning? (A) A model-free algorithm for value function approximation (B) A technique that estimates the value function using Monte Carlo methods (C) A method that optimizes the policy directly by adjusting the policy parameters (D) A technique that uses value iteration to improve the policy 16. What is the main advantage of using “Deep Reinforcement Learning”? (A) It can handle high-dimensional state and action spaces using neural networks (B) It requires less data compared to traditional RL algorithms (C) It simplifies the reward function (D) It guarantees convergence to the optimal policy 17. In the “Actor-Critic” method, what are the two main components? (A) The model, which predicts future states, and the actor, which selects actions (B) The critic, which updates the value function, and the model, which predicts rewards (C) The actor, which updates the policy, and the critic, which evaluates the policy (D) The value function, which estimates rewards, and the policy, which selects actions 18. What is “Monte Carlo Tree Search (MCTS)” used for in RL? (A) Planning and decision-making by simulating future actions and states (B) Estimating the Q-values of state-action pairs (C) Optimizing the policy directly using gradients (D) Learning the value function from experience 19. What does “SARSA” stand for in reinforcement learning? (A) State-Action-Random-State-Action (B) State-Action-Reward-State-Algorithm (C) State-Action-Return-State-Action (D) State-Action-Reward-State-Action 20. What is “Reward Shaping”? (A) Modifying the reward function to make learning easier or faster (B) Creating a model of the environment to predict future rewards (C) Adjusting the policy to maximize rewards (D) Using value iteration to update the value function 21. What does “Bootstrapping” refer to in reinforcement learning? (A) Exploring new actions to improve the policy (B) Estimating the reward of an action by using previous experiences (C) Updating the value function based on other estimates rather than waiting for the final outcome (D) Classifying states into categories for better policy learning 22. What is “Experience Replay” in deep reinforcement learning? (A) Adjusting the reward function based on previous outcomes (B) Replaying actions taken by the agent to improve exploration (C) Storing past experiences and reusing them to improve training efficiency (D) Simulating future states to update the value function 23. In the context of RL, what is a “Markov Decision Process (MDP)”? (A) An algorithm for updating the Q-values of state-action pairs (B) A method for optimizing policies in continuous action spaces (C) A mathematical framework for modeling decision-making in environments with stochastic transitions (D) A technique for feature extraction in high-dimensional state spaces 24. What is “Dynamic Programming” in reinforcement learning? (A) An approach for estimating future rewards using Monte Carlo methods (B) A technique for approximating the Q-values using neural networks (C) A method for sampling actions to explore the state space (D) A set of algorithms for solving MDPs by iteratively improving the value function and policy 25. Which of the following is a challenge in reinforcement learning? (A) High computational cost and data requirements (B) Simple model implementation (C) Easy reward function design (D) Low dimensional state and action spaces 26. What is “Double Q-Learning”? (A) A technique to reduce overestimation bias in Q-Learning by using two separate Q-value estimations (B) An approach for combining Q-Learning with SARSA (C) A method for optimizing the reward function using two separate models (D) A technique for enhancing exploration by using two different policies