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CS-456: Deep reinforcement learning

Summary

This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approaches, like PPO and DQN. The course will introduce students to practical applications of RL.

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/deep-reinforcement-learning-CS-456

Moodle Page

http://go.epfl.ch/CS-456

About this result

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Summary

Lectures

Instructors

Official source

https://edu.epfl.ch/coursebook/en/deep-reinforcement-learning-CS-456

Moodle Page

http://go.epfl.ch/CS-456

About this result

Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

Lectures in this course (96)

Exploration versus Exploitation

Explores the balance between exploring new possibilities and exploiting known rewarding actions in reinforcement learning.

Reinforcement Learning: SARSA Algorithm

Explores the SARSA algorithm for reinforcement learning, focusing on updating Q-values and the importance of exploration in learning by rewards.

Reinforcement Learning: BackUp Diagrams

Introduces the BackUp diagram as a key graphic representation in reinforcement learning.

Markov Decision Processes: Foundations of Reinforcement Learning

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Markov Decision Processes: Dynamic Programming Techniques

Discusses Markov Decision Processes and dynamic programming techniques for solving optimal policies in various scenarios.

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