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: -Reinforcement learning (RL) refers to a sub-field of machine learning that enables AI-based systems to take actions in a dynamic environment through trial and error to maximize the collective rewards based on the feedback generated for individual activities.

Detailed explanation-3: -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.

Detailed explanation-4: -To guide the learning process, reinforcement learning uses a scalar reward signal generated from the environment. This signal measures the performance of the agent with respect to the task goals. In other words, for a given observation (state), the reward measures the effectiveness of taking a particular action.