A robot is leaning how to move through a maze. The robot does not receive the correct path in advance. Instead, it ties different moves. It receives a reward when it gets closer to the exit and a penalty when it hits a wall.

Which type of machine leaning should be used?

Question

A robot is leaning how to move through a maze. The robot does not receive the correct path in advance. Instead, it ties different moves. It receives a reward when it gets closer to the exit and a penalty when it hits a wall.

Which type of machine leaning should be used?

BlackTom AI · Accepted Answer

Reinforcement learning trains an agent by trial-and-error using rewards and penalties, so it is the appropriate approach for the robot scenario.

1. Relevant knowledge points and concepts
- Reinforcement learning (RL): an agent interacts with an environment, takes actions, receives scalar rewards (positive for desirable outcomes, negative for undesirable), and learns a policy to maximize cumulative reward. Problems are often modeled as Markov Decision Processes (states, actions, transition probabilities, reward function).
- Supervised learning: requires labeled input-output pairs (correct answers) to learn a mapping.
- Unsupervised learning: finds structure or clusters in unlabeled data, no reward signal or actions.
- Semi-supervised learning: combines a small labeled set with a larger unlabeled set to improve supervised learning.

2. Step-by-step reasoning
- The robot does not receive the correct path in advance → there are no labeled correct action sequences, so supervised learning is not applicable.
- The robot tries different moves and receives feedback in the form of rewards when it nears the exit and penalties when it hits walls → this is exactly the reward/penalty signal that defines an RL problem.
- The goal is to learn a sequence of actions (a policy) that maximizes cumulative reward (reach exit while avoiding walls) → classic RL objective.

3. Calculations / operational steps
- Although no numeric calculations are required here, RL would formalize the problem: define state space (robot position, sensor readings), action space (move directions), reward function (+r for closer to exit, -p for hitting wall), and then apply an algorithm (e.g., Q-learning, SARSA, policy gradients) to update the policy to maximize expected return.

4. Why the correct answer is correct
- The core mechanism described—trial-and-error learning guided by rewards and penalties without pre-provided correct paths—is the defining characteristic of reinforcement learning.

5. Why other options are incorrect
- Supervised learning: requires correct path labels for training, which are not provided.
- Unsupervised learning: finds data structure but does not learn action policies from rewards/penalties.
- Semi-supervised learning: still relies on some labeled examples and is aimed at classification/regression, not sequential decision-making with reward-driven policy learning.

6. Conclusion
- Therefore, the correct choice is Reinforcement learning, which matches the provided answer.

A robot is leaning how to move through a maze. The robot does not receive the correct path in advance. Instead, it ties different moves. It receives a reward when it gets closer to the exit and a penalty when it hits a wall. Which type of machine leaning should be used?Single choice

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