GDSC IIT Indore

Reinforcement Learning is a type of Machine Learning where an agent learns to make decisions by interacting with an environment. It is based on the concept of trial and error learning, where the agent tries different actions and learns from the feedback it receives in the form of rewards or penalties. Reinforcement Learning is widely used in various domains such as gaming, robotics, finance, and healthcare. Reinforcement Learning Cycle The Reinforcement Learning process starts with an agent and an environment. The agent interacts with the environment by taking actions and receiving feedback in the form of rewards or penalties. The goal of the agent is to maximize its cumulative reward over a period of time. The agent uses a policy, which is a set of rules that determine the actions it takes in different situations. The policy is learned through trial and error, and it is updated based on the feedback received from the environment. The rewards and penalties in Reinforcement Learning are

Zero-Shot Learning Zero-shot learning is a problem setup in machine learning where, at test time, a learner observes samples from classes that were not observed during training and needs to predict the class they belong to. The general idea of zero-shot learning is to transfer the knowledge in the training instances to test instance classification. Thus, zero-shot learning is a subfield of transfer learning. Zero-shot learning has applications in image classification, natural language processing, and more. Zero-shot learning has many potential applications in domains where labeled data is scarce or expensive, such as medical imaging, natural language understanding, speech recognition, etc. This is useful for scenarios where obtaining labeled data for every possible class is impractical or impossible, such as classifying all animal species or natural languages. One of the challenges of zero-shot learning is representing unseen classes so that the model can understand and relate to the s