COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Learning from labeled data
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Learning from unlabeled data
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Learning from environment
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None of the above
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Detailed explanation-1: -Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.
Detailed explanation-2: -The reinforcement learning problem is meant to be a straightforward framing of the problem of learning from interaction to achieve a goal. The learner and decision-maker is called the agent. The thing it interacts with, comprising everything outside the agent, is called the environment.
Detailed explanation-3: -What is Environment in Reinforcement Learning? In reinforcement learning, Environment is the Agent’s world in which it lives and interacts. The agent can interact with the environment by performing some action but cannot influence the rules or dynamics of the environment by those actions.
Detailed explanation-4: -Hence, we can say that “Reinforcement learning is a type of machine learning method where an intelligent agent (computer program) interacts with the environment and learns to act within that.” How a Robotic dog learns the movement of his arms is an example of Reinforcement learning.