Reinforcement Learning Basics Answers 1. What defines a model-free reinforcement learning algorithm? A. It directly learns the optimal policy or value function through interaction. B. It relies primarily on theoretical calculations rather than empirical data. C. It learns a model of the environment before making decisions. D. It requires significant pre-training and is sample inefficient. Answer: It directly learns the optimal policy or value function through interaction. (A) Model-free algorithms learn directly from the environment without creating a model. 2. Which of the following is NOT a characteristic of model-based reinforcement learning algorithms? A. They plan and choose actions based on the learned model. B. They can improve sample efficiency by simulating actions. C. They focus solely on maximizing immediate rewards. D. They learn a model of the environment. Answer: They focus solely on maximizing immediate rewards. (C) While model-based algorithms can maximize long-term rewards, their focus on learning models distinguishes them from other approaches. 3. What is a common challenge faced in reinforcement learning? A. Simplicity of modeling environmental dynamics. B. Limited capability of algorithms to exploit learned knowledge. C. Avoidance of large action spaces. D. Exploration-exploitation dilemma requiring balance. Answer: Exploration-exploitation dilemma requiring balance. (D) The exploration-exploitation dilemma is a significant challenge that requires navigating between trying new actions and utilizing known beneficial actions. 4. Which application is an example of reinforcement learning in use? A. Statistical analysis of data sets. B. Image recognition in neural networks. C. Game playing like AlphaGo. D. Content creation for social media. Answer: Game playing like AlphaGo. (C) AlphaGo is a well-known example of reinforcement learning applied in the field of game playing. 5. What does sample efficiency refer to in the context of reinforcement learning? A. The requirement of extensive interactions with the environment to learn effectively. B. The ability to learn a good policy using relatively few interactions. C. The number of successful actions taken during training. D. The ability to make accurate predictions without data. Answer: The ability to learn a good policy using relatively few interactions. (B) Sample efficiency is vital as it impacts how quickly and effectively an algorithm can learn an optimal policy. 6. What is the primary goal of reinforcement learning for an agent? A. To maintain a static policy throughout interactions B. To minimize the number of actions taken C. To create a model of the environment D. To maximize cumulative rewards over time Answer: To maximize cumulative rewards over time (D) The main goal of reinforcement learning is for the agent to maximize the expected cumulative rewards through interactions with the environment. 7. Which of the following best describes a state in reinforcement learning? A. The cumulative reward received at the end of a task B. A specific action taken by the agent C. The current situation of the environment D. The policy that the agent follows Answer: The current situation of the environment (C) A state represents the current situation of the environment in which the agent operates and makes decisions. 8. What defines the behavior of an agent in reinforcement learning? A. The current state of the environment B. The trial-and-error learning process C. The rewards received from actions D. The policy mapping states to actions Answer: The policy mapping states to actions (D) The policy is what defines the agent's behavior by mapping states to the probabilities of taking various actions. 9. In reinforcement learning, what distinguishes a model-based agent from a model-free agent? A. Model-based agents learn a model of the environment B. Model-based agents do not interact with the environment C. Model-based agents do not receive rewards D. Model-based agents follow a static policy Answer: Model-based agents learn a model of the environment (A) Model-based reinforcement learning agents attempt to learn a model of the environment to simulate future scenarios, distinguishing them from model-free agents. 10. What role do value functions play in reinforcement learning? A. To map states to specific actions B. To estimate the long-term value of states or actions C. To define the environment's possible states D. To calculate immediate rewards only Answer: To estimate the long-term value of states or actions (B) Value functions estimate the long-term value of being in a given state or taking a specific action, which is fundamental for many RL algorithms. 11. Which type of policy always selects the same action for a given state? A. Adaptive policy B. Deterministic policy C. Stochastic policy D. Dynamic policy Answer: Deterministic policy (B) A deterministic policy consistently selects the same action for a given state, as opposed to a stochastic policy which selects actions probabilistically. 12. How do agents learn to map states to actions in reinforcement learning? A. By imitating another agent's policy B. Using a static model of the environment C. By receiving pre-defined actions D. Through trial and error methods Answer: Through trial and error methods (D) Agents in reinforcement learning learn through trial and error, receiving feedback in the form of rewards for the actions they take. 13. What is true about the rewards in a reinforcement learning framework? A. Rewards are only given after completing a task B. Rewards must always be positive C. Rewards can be negative, providing a detriment for actions D. Rewards are not a factor in the learning process Answer: Rewards can be negative, providing a detriment for actions (C) In reinforcement learning, rewards can be positive or negative, quantifying the immediate benefit or detriment from taking an action.