This workshop on reinforcement learning (RL) seeks to address the multifaceted challenges and transformative opportunities that lie at the intersection of RL theory, algorithms, data, and real-world applications. In recent years, RL has garnered significant attention across a broad spectrum of disciplines, including robotics, healthcare, energy systems, and transportation, underscoring its potential to revolutionize various fields. However, the gap between theoretical advances in RL and their practical deployment in complex, real-world environments remains substantial. This workshop aims to bridge this gap by bringing together a diverse group of leading experts from computer science, control theory, machine learning, and domain-specific fields. Through focused discussions on key areas such as aligning RL theory with practical applications, incorporating domain structures into RL, applying RL in real-world contexts, and refining benchmarks and performance metrics, the workshop will serve as a catalyst for interdisciplinary collaboration. The ultimate goal is to shape the future research paradigm in RL, fostering innovations that are both theoretically sound and practically viable.