Text-to-speech (TTS) synthesis is an important component in speech technology that converts written text into spoken words, enabling machines to communicate with humans through voice. This chapter explores how reinforcement learning (RL) is transforming TTS systems, addressing challenges such as naturalness, expressiveness, and real-time generation. We’ll examine innovative RL-based approaches that are making TTS systems more human-like, adaptable, and efficient. Through case studies and practical examples, we’ll demonstrate how these advancements are enhancing applications ranging from virtual assistants to accessibility tools for the visually impaired. We’ll also investigate how RL is being applied to tackle the unique challenges of incremental TTS, enabling more responsive and interactive voice interfaces. As we delve into this exciting field, we’ll explore how improvements in TTS synergize with other areas of speech and language technology, paving the way for more natural and engaging human-machine interactions.