Types of Reinforcement Learning Answers 1. What is the primary benefit of using a behavior policy different from the target policy? A. It reduces computational costs significantly. B. It simplifies the learning process. C. It ensures that the agent learns more intuitively. D. It allows for the collection of diverse samples. Answer: It allows for the collection of diverse samples. (D) Using a behavior policy provides the agent with varied experiences, enhancing exploration and learning efficiency. 2. Which method is typically NOT associated with linear function approximation? A. Simple linear regression B. Multiple linear regression C. Polynomial regression models D. Deep learning models Answer: Deep learning models (D) Deep learning models are examples of non-linear function approximation methods, not linear ones. 3. What could be a significant limitation when using linear function approximation in complex environments? A. It cannot represent non-linear relationships effectively. B. It often leads to overfitting. C. It is always slower than deep learning models. D. It requires excessive computational resources. Answer: It cannot represent non-linear relationships effectively. (A) Linear function approximation struggles to model complex, non-linear relationships in intricate environments. 4. Which of the following represents a characteristic of non-linear function approximation? A. It always requires less data to train. B. It can adapt to high-dimensional environments. C. It is computationally simpler than linear methods. D. It cannot handle complex mappings. Answer: It can adapt to high-dimensional environments. (B) Non-linear function approximation, such as deep learning, can effectively handle high-dimensional environments and complex mappings. 5. What is one major downside of using deep learning for function approximation? A. It often leads to less flexible models. B. It only works in low-dimensional spaces. C. It requires less computational power. D. It can be slow to train. Answer: It can be slow to train. (D) Deep learning models are often more computationally demanding to train, which can slow down the learning process.