Multiple-choice reading activity

What are the advantages of reinforcement learning?

• Suitable for solving simple problems.

• Requires minimal data and computation.

• Handles only deterministic environments.

• Can solve complex problems and adapts to non-deterministic environments.

How does reinforcement learning differ from supervised learning?

• Reinforcement learning uses labeled training data.

• Supervised learning involves trial-and-error learning.

• Reinforcement learning has no answer key during training.

• Supervised learning maximizes rewards.

What is the credit assignment problem in reinforcement learning?

• Assigning rewards based on the environment's initial state.

• Allocating credits to the learning agent for future actions.

• Assessing the importance of intermediate states in reaching the goal.

• Determining the value of the reward function.

What is the role of a reward function in reinforcement learning?

• Defines a goal in a reinforcement learning problem.

• Provides a numerical score based on the state of the environment.

• Specifies the optimal behavior of the learning agent.

• Determines the initial state for the model.

What is the primary goal of reinforcement learning?

• Supervised learning

• Unsupervised learning

• Minimize reward

• Maximize reward