Reinforcement Learning (RL) is of increasing relevance today, including in games, complex energy systems, recommendation engines, finance, logistics, transport, and for auto-tuning the parameters of large learning frameworks such as LLMs. In this course we study modern state-of-the-art reinforcement learning algorithms and related approaches (decision transformers, transfer learning, imitation learning, inverse reinforcement learning, ...). A focus of the course is on RL solutions with deep neural architectures that can scale to modern applications, and other aspects that are concerned in real-world deployments (safety, interpretability, ...).
