This immersive 5-day workshop offers a comprehensive and hands-on introduction to reinforcement learning (RL), designed to bridge the gap between theoretical foundations and real-world applications — including those critical to building a more sustainable future. Ideal for students, educators, and professionals, this workshop emphasizes experiential learning through clear conceptual explanations, coding exercises, and project-based tasks.

Participants will explore key RL concepts such as Markov Decision Processes, policy optimization, and deep RL, while applying them to impactful domains like energy optimization, smart transportation, resource-efficient robotics, and climate-aware decision systems. The course incorporates interactive notebooks and algorithm implementations in Python using frameworks such as Gymnasium, PyTorch, and Stable-Baselines3. Beyond just learning, participants will engage with open-ended projects that reflect sustainability challenges and explore how intelligent agents can support responsible, long-term decision-making. The workshop encourages reproducible research and real-world innovation aligned with global sustainability goals.