Descriptif
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, ...).
Objectifs pédagogiques
Students should learn to design and deploy deep learning methodologies and architectures for a variety of contexts in involving reinforcement learning algorithms (and related algorithms), and develop a strong intuition, based on theoretical and practical insights, to their performance and limitations.
Diplôme(s) concerné(s)
Parcours de rattachement
Format des notes
Numérique sur 20Littérale/grade réduitPour les étudiants du diplôme M2 DATAAI - Data and Artificial Intelligence
Programme détaillé
The course is roughly organised into four approaches to the theme of depth in Deep Reinforcement Learning:,
1. Depth in value function (DQN and variants, distributional RL, ...),
2. Depth in policy (PPO, SAC, imitation learning, ...),
3. Depth in environment model (Monte Carlo Tree Search, model-based reinforcement learning),
4. Depth in reward model (reward shaping, inverse reinforcement learning, transfer learning ...).
