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