Which learning type is used when the system interacts with an environment and learns through rewards and penalties?

Question

BlackTom AI · Accepted Answer

Reinforcement learning trains an agent to make decisions by interacting with an environment and receiving scalar feedback in the form of rewards and penalties.

1) Relevant knowledge points and concepts
- Reinforcement learning (RL): an agent observes a state, takes actions, receives rewards (positive/negative), and updates its policy or value estimates to maximize cumulative reward over time.
- Supervised learning: learns a mapping from inputs to labeled outputs using explicit target labels for each example.
- Unsupervised learning: discovers structure or patterns in unlabeled data (clustering, dimensionality reduction).
- Self-supervised learning: creates surrogate labels from the data itself to learn useful representations without external labels.

2) Step-by-step reasoning process
Step 1: Identify the key phrase in the question: “system interacts with an environment and learns through rewards and penalties.”  
Step 2: Match this description to the definitions above. RL explicitly describes interaction with an environment and learning driven by reward signals.  
Step 3: Contrast alternatives: supervised and unsupervised do not rely on sequential interactions with an environment and do not use reward/penalty feedback as their learning signal. Self-supervised also lacks an interacting agent receiving external rewards.

3) Intermediate procedural steps (RL loop)
- Observe state s_t.
- Choose action a_t according to policy π(a|s).
- Environment returns new state s_{t+1} and reward r_{t}.
- Update policy/value based on r_{t} to improve future cumulative reward.

4) Why the correct answer is correct
The defining characteristic—learning by trial, interaction, and scalar reward feedback—is the hallmark of reinforcement learning. The mechanism of penalties and rewards guiding behavior directly matches RL frameworks (e.g., Q-learning, policy gradients).

5) Why other options are incorrect (brief)
- Supervised learning: requires labeled input-output pairs, not sequential environment interaction with rewards.  
- Unsupervised learning: finds patterns without external rewards or actions affecting an environment.  
- Self-supervised learning: generates internal proxy labels from data (e.g., masked tokens) rather than learning from rewards/penalties via environmental interaction.

6) Conclusion
Therefore, the correct choice is Reinforcement learning. This matches the provided answer: Reinforcement learning.

Which learning type is used when the system interacts with an environment and learns through rewards and penalties?单项选择题

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