MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Reinforcement Learning is used in multiple areas of NLP like text summarization, question answering, translation, dialogue generation, machine translation etc. Reinforcement Learning agents can be trained to understand a few sentences of the document and use it to answer the corresponding questions.

Detailed explanation-2: -Some common application of reinforcement learning examples include industry automation, self-driving car technology, applications that use Natural Language Processing, robotics manipulation, and more.

Detailed explanation-3: -Some of the autonomous driving tasks where reinforcement learning could be applied include trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways. For example, parking can be achieved by learning automatic parking policies.

Detailed explanation-4: -Reinforcement Learning and Computer Vision Computer Vision has been making rapid progress recently, and deep learning plays an important role. Reinforcement learning is an effective tool for many computer vision problems, like image classification, object detection, face detection, captioning, and more.

Detailed explanation-5: -Three approaches to Reinforcement Learning Now that we defined the main elements of Reinforcement Learning, let’s move on to the three approaches to solve a Reinforcement Learning problem. These are value-based, policy-based, and model-based.