Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards. Unlike supervised learning, which relies on labeled data, RL uses a trial-and-error approach, where the agent explores various actions and receives feedback in the form of rewards or penalties. This feedback helps the agent improve its strategy, known as a policy, over time. Key components of RL include states, actions, rewards, and the policy that maps states to actions. Popular algorithms in RL include Q-learning, Deep Q-Networks (DQN), and Policy Gradient methods. RL has applications in diverse fields such as robotics, game playing, and autonomous vehicles, where it enables systems to adapt and optimize their performance in complex, dynamic environments. This book elucidates new techniques and their applications of Reinforcement Learning in a multidisciplinary manner. This book is compiled in such a manner, that it will provide in-depth knowledge about the theory and practice of Reinforcement Learning. The extensive content of this book provides the readers with a thorough understanding of the subject.