EMERGING TRENDS IN COMPUTING
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
The process of learning an action through feedback and practice until the action is completed.
|
|
|
The process of learning with training labels (like donut or bagel from the crash course video).
|
|
|
The process of learning without training labels through grouping and clustering things that are similar.
|
|
|
None of the above
|
Detailed explanation-1: -Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty.
Detailed explanation-2: -Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
Detailed explanation-3: -The agent observes an input state. An action is determined by a decision making function (policy) The action is performed. The agent receives a scalar reward or reinforcement from the environment. Information about the reward given for that state / action pair is recorded.