An engineering team is training a warehouse robot to navigate around shelving units. The robot has no labeled dataset of correct paths; instead it repeatedly attempts routes and receives a positive score when it reaches the target faster and a penalty when it collides with obstacles, gradually improving its behavior over thousands of attempts. Which type of machine learning does this describe?
Select an answer to reveal the explanation.
Short Explanation
Picture a robot bumping around a warehouse with nobody handing it a rulebook of correct routes. You're seeing trial-and-error learning where quick target-reaching earns reward and collisions earn penalty, so it gradually favors moves that pay off. That reward-driven loop is reinforcement learning, not learning from labeled examples, pattern discovery with no feedback, or predicting a continuous value.
Full Explanation
The correct answer is reinforcement learning. The robot has no dataset of correct paths to learn from; instead it takes actions, observes a reward when it reaches the target quickly and a penalty when it collides with obstacles, and adjusts its behavior over many trials based on that feedback — this trial-and-reward learning loop is the defining trait of reinforcement learning. Supervised learning is incorrect because it requires a labeled dataset of input-output pairs, such as example paths marked correct or incorrect, which does not exist here. Unsupervised learning is incorrect because it looks for structure or groupings within existing data without any feedback signal, whereas the robot is actively receiving rewards and penalties tied to its own actions. Regression is incorrect because it is a supervised technique for predicting a continuous numeric value from labeled training examples, and the robot is not predicting a number from historical data, it is improving behavior through consequences of its own actions.