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
Pour les étudiants du diplôme MScT-Artificial Intelligence and Advanced Visual Computing
This course assumes familiarity with the foundations of RL and its main paradigms (temporal-difference learning, Monte Carlo, and policy-gradient methods), covered in a course such as CSC_52081_EP Reinforcement Learning and Autonomous Agents (in the 3A programme).
Format des notes
Numérique sur 20Littérale/grade réduitPour les étudiants du diplôme MScT-Artificial Intelligence and Advanced Visual Computing
Vos modalités d'acquisition :
Oral exam (presentation and discussion format), which covers a discussion of lab assignments and lecture material, and a choice of research paper, throughout the course.
Le rattrapage est autorisé (Max entre les deux notes)- Crédits ECTS acquis : 2 ECTS
La note obtenue rentre dans le calcul de votre GPA.
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 ...).