User Tools

Site Tools


curriculum

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
Last revision Both sides next revision
curriculum [2023/04/27 10:31]
scornet [Scientific courses, period 1 (October - December)]
curriculum [2023/06/22 18:40]
scornet [Scientific Courses, period 2 (January - March)]
Line 83: Line 83:
 ==== Scientific Courses, period 2 (January - March) ==== ==== Scientific Courses, period 2 (January - March) ====
  
-**MAP/​INF641 ​- Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu)+**INF649 ​- Deep Reinforcement Learning (24h, 2 ECTS), Jesse Read** (contact: jesse.read@polytechnique.edu)
    ​Reinforcement learning (RL) is of increasing relevance today, including in games, complex energy systems, recommendation engines, finance, logistics, and for auto-tuning the parameters of other learning frameworks. This course assumes familiarity with the foundations of RL and its main paradigms (temporal-difference learning, Monte Carlo, and policy-gradient methods). We will explore them further, and study modern state-of-the-art variants (such as proximal policy optimization),​ with a focus on developing RL solutions with deep neural architectures suited to modern applications. We will also take a look at specialized topics such inverse reinforcement learning.    ​Reinforcement learning (RL) is of increasing relevance today, including in games, complex energy systems, recommendation engines, finance, logistics, and for auto-tuning the parameters of other learning frameworks. This course assumes familiarity with the foundations of RL and its main paradigms (temporal-difference learning, Monte Carlo, and policy-gradient methods). We will explore them further, and study modern state-of-the-art variants (such as proximal policy optimization),​ with a focus on developing RL solutions with deep neural architectures suited to modern applications. We will also take a look at specialized topics such inverse reinforcement learning.
  
Line 112: Line 112:
  
  
-==== Final project (April to September - 30 ECTS) ==== +==== Final project (April to September - 32 ECTS) ==== 
 MAP/INF690 - Internship: 5 to 6 months project, either in the R&D department of a company or in a research lab. MAP/INF690 - Internship: 5 to 6 months project, either in the R&D department of a company or in a research lab.
  
  
curriculum.txt ยท Last modified: 2023/06/22 18:41 by scornet