RL

What is “temporal difference learning” in reinforcement learning? a) A supervised learning method b) A way to learn the difference between consecutive states c) A combination of Monte Carlo methods and dynamic programming d) A method to learn from a fixed dataset Answer: c) A combination of Monte Carlo methods and dynamic programming Explanation: Temporal […]

What is deep reinforcement learning? a) Combining supervised learning with reinforcement learning b) Using deep neural networks to approximate value functions or policies c) A reinforcement learning model that requires labeled data d) A method to generate new training data Answer: b) Using deep neural networks to approximate value functions or policies Explanation: Deep reinforcement

What is the exploration-exploitation tradeoff in reinforcement learning? a) A balance between trying new actions and using known actions to maximize rewards b) A way to decrease the loss function c) A method to avoid overfitting d) A function used to update the policy Answer: a) A balance between trying new actions and using known

What is “value function” in reinforcement learning? a) A function that predicts rewards based on future actions b) A function that maps states to the expected future rewards c) A function that reduces overfitting d) A function that classifies the action space Answer: b) A function that maps states to the expected future rewards Explanation:

Which of the following is an example of reinforcement learning? a) Classifying emails as spam or not spam b) Training a robot to navigate through a maze c) Detecting objects in an image d) Predicting house prices Answer: b) Training a robot to navigate through a maze Explanation: Reinforcement learning is often used in situations

What is “model-free” reinforcement learning? a) Learning without relying on a model of the environment b) Learning by building a model of the environment c) Learning with supervised data d) Learning with predefined labels Answer: a) Learning without relying on a model of the environment Explanation: Model-free reinforcement learning refers to approaches where the agent

What is overfitting in the context of reinforcement learning? a) When the agent only learns specific situations and fails to generalize b) When the agent learns the optimal policy c) When the agent explores all possible states d) When the agent reaches the terminal state Answer: a) When the agent only learns specific situations and

What is an episode in reinforcement learning? a) A set of data points used for training b) A sequence of actions and states from the start to the end of a task c) A deep learning model architecture d) A method for reducing dimensionality Answer: b) A sequence of actions and states from the start

What is Q-learning in reinforcement learning? a) A model-free algorithm used to learn the value of actions b) A type of supervised learning c) A deep learning algorithm d) A classification method for actions Answer: a) A model-free algorithm used to learn the value of actions Explanation: Q-learning is a model-free reinforcement learning algorithm that

What does the Bellman equation describe in reinforcement learning? a) The relation between current and future rewards b) The total number of states in the environment c) The accuracy of a supervised learning model d) The number of possible actions Answer: a) The relation between current and future rewards Explanation: The Bellman equation describes the

What is the role of the discount factor in reinforcement learning? a) To give higher weight to future rewards b) To reduce the importance of future rewards compared to immediate rewards c) To balance the action space d) To increase the loss function Answer: b) To reduce the importance of future rewards compared to immediate

What is the “Markov decision process” (MDP) in reinforcement learning? a) A process used to make supervised learning predictions b) A mathematical framework to model decision-making with rewards and states c) An algorithm for training deep neural networks d) A system for classifying data Answer: b) A mathematical framework to model decision-making with rewards and

What is the difference between reinforcement learning and supervised learning? a) Reinforcement learning uses labeled data, while supervised learning uses trial and error b) Reinforcement learning uses trial and error, while supervised learning uses labeled data c) Both methods use labeled data d) Both methods involve trial and error Answer: b) Reinforcement learning uses trial

What does the term “exploitation” mean in reinforcement learning? a) Using the agent’s existing knowledge to make decisions b) Searching for new states c) Reducing the action space d) Resetting the environment Answer: a) Using the agent’s existing knowledge to make decisions Explanation: Exploitation refers to using the agent’s learned policy or knowledge to make

What is the role of exploration in reinforcement learning? a) To ensure the agent always follows the best-known policy b) To allow the agent to try new actions and learn from them c) To increase the loss function d) To minimize the number of states in the environment Answer: b) To allow the agent to

What does the term “policy” mean in reinforcement learning? a) The function that maps actions to rewards b) The strategy that defines how the agent selects actions c) The model used to predict future states d) The algorithm used to update the environment Answer: b) The strategy that defines how the agent selects actions Explanation:

What is a reward in reinforcement learning? a) The total number of actions taken b) A signal given to the agent to indicate how good or bad an action is c) The loss value of the agent’s model d) The final state of the environment Answer: b) A signal given to the agent to indicate

In reinforcement learning, what does the environment refer to? a) The training data b) The external system with which the agent interacts c) A type of algorithm d) The loss function Answer: b) The external system with which the agent interacts Explanation: The environment in reinforcement learning is the external system that the agent interacts

What is an agent in reinforcement learning? a) A system that provides rewards b) A function that takes inputs c) An entity that interacts with the environment and takes actions d) A type of neural network Answer: c) An entity that interacts with the environment and takes actions Explanation: The agent is the learner in

What is the goal of reinforcement learning? a) To minimize the loss function b) To maximize cumulative rewards over time c) To reduce the number of features d) To generate labeled data Answer: b) To maximize cumulative rewards over time Explanation: The goal of reinforcement learning is to train agents to take actions that maximize