Question

question: 4
identify which type of machine learning (ml) best describes the scenario below:
a new tetris video game wants to include computer players that the human players can play against. they allow their ml system to play the game thousands of times, giving it point rewards every time it successfully clears the lines and survives another 30 seconds. once trained, the computer player can beat many beginner and intermediate players!
supervised learning
unsupervised learning
reinforcement learning
none of the above

Explanation:

In reinforcement learning, an agent learns to make a sequence of decisions by interacting with an environment and receiving rewards or penalties. Here, the ML system plays Tetris, gets point - rewards for clearing lines and surviving, which is characteristic of reinforcement learning. Supervised learning requires labeled data for training, and unsupervised learning looks for patterns in unlabeled data. This scenario doesn't match those.

Answer:

C. Reinforcement Learning