Spoken language understanding (SLU) is a critical technology that bridges the gap between speech recognition and language comprehension, enabling machines to extract meaning from spoken language. This chapter explores how reinforcement learning (RL) is revolutionizing SLU systems, addressing challenges such as adapting to diverse speaking styles, handling ambiguity in spoken language, and integrating context from multi-turn dialogues. We’ll examine innovative RL-based approaches that are making SLU systems more accurate, robust, and adaptable to real-world scenarios. Through case studies and practical examples, we’ll demonstrate how these advancements are enhancing voice-based interfaces, from virtual assistants to interactive voice response systems. As we delve into this exciting field, we’ll also investigate how improvements in SLU synergize with other areas of speech and language technology, paving the way for more natural and intelligent voice-based interactions.