MCQ IN COMPUTER SCIENCE & ENGINEERING

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: -In reinforcement learning, the agent is rewarded for taking controls that lead to successful states. The rewards can be immediate, such as receiving a point for each step taken in the right direction, or they can be delayed, such as receiving a point at the end of the episode if the goal was reached.

Detailed explanation-3: -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. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Detailed explanation-4: -Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.