Reinforced Learning: What's It is, Why Important, Where Applied, When to Use, How It Works, Types, Advantages, Disadvantages + Many More to Understand !!
Abstract : Reinforcement Learning (RL) is a machine learning technique that teaches agents how to make decisions by interacting with an environment to achieve a goal. It's based on the Markov decision process, a mathematical model that uses discrete time steps to model decision-making. Here are some key points about reinforcement learning: How it works RL agents learn to perform tasks by trying different strategies to maximize rewards based on feedback. The agent learns the optimal behavior in an environment by observing how it responds to its actions. How it's used RL is used in artificial intelligence (AI) to direct unsupervised machine learning. It can be used to improve energy efficiency, reduce downtime, and increase equipment longevity. For example, Google's DeepMind team developed RL models that helped reduce energy consumption for cooling data centers by up to 40%. How it's similar to human learning RL is similar to how children learn the