Reinforcement Learning is a subfield of Machine Learning in which an agent explores an environment to learn how to perform specific tasks by taking actions with a good outcome and avoiding those with a bad one. A reinforcement learning model will learn from its experiences and will identify which actions lead to the best rewards. In reinforcement learning, the agent takes action based on the state of the environment, and the environment will return the reward and the next state. The agent employs a trial and error method to learn. It initially takes random actions and identifies which actions lead to long-term rewards over time.

I hope that you've come across, from algorithms achieving super-human level performance at Atari 2600 games, beating professional players at GO, Dota 2 and StarCraft II and to algorithms controlling nuclear-fusion reactors. These are the success stories of reinforcement learning algorithms combined with deep learning (Deep Reinforcement learning or DeepRL). Google's DeepMind and OpenAI heavily does research in this area and thinks that DeepRL is the future of AI. Some Researchers even think that RL might be the key to Artificial General Intelligence (AGI). Reinforcement Learning (RL) is one of the paradigms of Machine learning along with supervised and unsupervised learning.

Here,maze will act like an environment and our bot will be an agent which can perform certain action like moving along up, down,left,right. Performing an action also cause to change of state of our bot. Now,suppose out bot is trying to explore the environment.In the meantime,our bot will perform action,in turn it will get reward or punishment(negative reward) and by doing that process it's going to be learning about what was going to be exploring the environment, understanding what actions leads to good rewards and favourable states and what action leads to bad rewards and unfavorable state.

Decision intelligence focuses on making more accurate and more efficient decisions based on the knowledge of how actions lead to outcomes. If you've ever been faced with decision fatigue over what to wear in the morning or gotten frustrated with a group's lack of consensus over where to eat for lunch, you understand how crucial time can be in decision-making. Decision intelligence, a crucial field of data analytics, aims to reduce the time to decision and help eliminate the uncertainty organizations can be making changes. Decision intelligence is officially on the hype cycle. Gartner proclaims it a top data and analytics trend for 2021, but we predict it will move quickly from trend to established principle.

Today, accomplishing more with less is a key rule that drives business strategy across numerous resource-intensive industries. Organizations are hoping to get a better yield out of artificial intelligence (AI) and machine learning (ML) than simply extraordinary insights. They need access to proposals that help rearrange complex choices around how scarce resources ought to be allotted, how to plan tasks, and how to manage limitations. The unpredictability of the results in today's decision models regularly emerges from the powerlessness to catch the vulnerability factors connected to these models' behavior in a business setting. By introducing machine learning algorithms with decision-making processes, another field called "decision intelligence" is rising to make strong decision models in a wide scope of processes.