Final answer:
Value iteration is indeed guaranteed to converge if the discount factor γ is between 0 and 1, which reinforces planning for future rewards in reinforcement learning.
Step-by-step explanation:
The student's question asks whether it's true or false that value iteration is guaranteed to converge if the discount factor is between 0 and 1 (0 < γ < 1). The answer to this question is True. Value iteration is a method used in reinforcement learning, which is a subset of machine learning within artificial intelligence. This method iteratively updates the values of states in a decision process to find optimal policies. The condition that the discount factor γ should be between 0 and 1 is crucial because it ensures that future rewards are discounted over time, thus making the infinite sum of rewards converge. If γ is equal to or greater than 1, there can be no guarantee of convergence as the rewards could continue to increase indefinitely without a discount.