Financial Risk Manager Part 1
Get started today
Ultimate access to all questions.
Which of the following statements is the most accurate?
Exam-Like
Community
TTanishq
A
In reinforcement learning, the algorithm is trained on a smaller dataset and is expected to generalize to new situations, while in supervised learning the algorithm is trained on a larger dataset and is expected to perform well on the training data.
B
In reinforcement learning, the algorithm is given correct output values, similar to how supervised learning algorithms are presented with labeled data.
C
Reinforcement learning is similar to supervised learning in that the algorithm must learn through trial and error but is not presented with the correct output values for the training set.
D
Reinforcement learning is not based on labeled training data, but rather it uses feedback in the form of rewards or punishments to learn how to take actions in an environment to maximize a reward
Explanation:
Explanation
Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some type of reward. Unlike supervised learning, reinforcement learning does not rely on labeled training data. Instead, the agent learns from the consequences of its actions, which are presented as rewards or punishments.
Key Characteristics of Reinforcement Learning:
- No labeled data: Unlike supervised learning, there are no pre-labeled input-output pairs
- Feedback-based learning: Uses rewards and punishments as feedback signals
- Environment interaction: Learns through trial and error by interacting with the environment
- Policy optimization: Goal is to learn a policy that maximizes cumulative reward over time
Why Other Options Are Incorrect:
Choice A: Incorrect because dataset size doesn't fundamentally distinguish reinforcement learning from supervised learning. Both can use large or small datasets depending on the application.
Choice B: Incorrect because reinforcement learning does NOT receive correct output values like supervised learning. It receives reward/punishment signals instead.
Choice C: Incorrect because reinforcement learning doesn't use a traditional "training set" at all - it learns through continuous interaction with the environment rather than processing a predefined dataset.
Choice D is correct because it accurately describes the core mechanism of reinforcement learning: learning through environmental feedback (rewards/punishments) without labeled training data.
Comments
Loading comments...