This book offers a comprehensive guide to reinforcement learning (RL) and bandits for speech and language technology. The book first provides an overview of RL and bandit methods and their applications to various speech and language tasks. The author then covers essential topics such as the formulations for speech and language tasks into RL problems, RL-based solutions in automatic speech recognition, speaker recognition, diarization, natural language understanding, text-to-speech synthesis, natural language generation, and conversational recommendation systems. The book also presents emerging strategies in RL methods, along with open questions and challenges in RL-based speech and language technology. With a focus on real-world applications, the book provides step-by-step guidance on how to use RL and bandit methods to solve problems in speech and language technology. The book also includes case studies and practical tips to help readers apply RL and bandit methods to their own projects. The book is a timely resource for speech and language researchers, engineers, students, and practitioners who are interested in learning how RL methods can improve the performance of speech and language systems and provide new interactive learning paradigms from an interface design point of view.