Modern Reinforcement-learning using Deep Learning

Course Feature
  • Cost
    Free
  • Provider
    Udemy
  • Certificate
    No Information
  • Language
    English
  • Start Date
    2022-06-20
  • Learners
    No Information
  • Duration
    No Information
  • Instructor
    Nitsan Soffair
Next Course
1.4
2,771 Ratings
Nitsan Soffair, a Deep RL researcher at BGU, is offering a course on the newest state-of-the-art Deep reinforcement-learning knowledge. In this course, you will learn about model types, algorithms and approaches, function approximation, deep reinforcement-learning, and deep multi-agent reinforcement-learning. You will also be able to validate your knowledge by answering short and very short quizzes of each lecture. The course can be completed in approximately two hours. Don't miss out on this opportunity to learn the latest in Deep RL!
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Modern Reinforcement-learning using Deep Learning Go to class
Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 21st, 2023]

What skills and knowledge will you acquire during this course?
Students will acquire skills and knowledge in Model types, Algorithms and approaches, Function approximation, Deep reinforcement-learning, and Deep Multi-agent Reinforcement-learning. They will also gain knowledge in topics such as Markov decision process (MDP), Partially observable Markov decision process (POMDP), Decentralized Partially observable Markov decision process (Dec-POMDP), Bellman equations, Model-free, Off-policy, Exploration-exploitation, Value-iteration, SARSA, Q-learning, Function approximators, Policy-gradient, Value-based, Policy-based, Actor-critic, policy-gradient, and softmax policy, REINFORCE, Deep Q-Network (DQN), Deep Recurrent Q-Learning (DRQN), Optimistic Exploration with Pessimistic Initialization (OPIQ), Value Decomposition Networks (VDN), QMIX, QTRAN, and Weighted QMIX.

How does this course contribute to professional growth?
This course on Modern Reinforcement-learning using Deep Learning contributes to professional growth by providing students with state-of-the-art knowledge and skills in various aspects of reinforcement-learning. Students will gain expertise in model types, algorithms, function approximation, deep reinforcement-learning, and deep multi-agent reinforcement-learning. By completing short quizzes, students can validate their understanding of the lectures. The course covers a wide range of topics, including Markov decision process, Bellman equations, different learning methods, and various deep learning techniques. The resources provided, such as Wikipedia and David Silver's Reinforcement-learning course, further enhance the learning experience. Overall, this course equips students with valuable knowledge and skills that can contribute to their professional growth in the field of reinforcement-learning.

Is this course suitable for preparing further education?
This course on Modern Reinforcement-learning using Deep Learning is suitable for preparing further education.

Course Syllabus

Model types

Algorithms and approaches

Function approximation

Deep reinforcement-learning

Deep Multi-agent Reinforcement-learning

Extra content

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Pros & Cons
  • The course provides a comprehensive introduction to modern reinforcement learning techniques, specifically focusing on deep learning. The instructor covers a wide range of topics and provides in-depth explanations, making it easy to understand complex concepts.
  • The course offers practical examples and hands-on exercises that allow students to apply the learned concepts in real-world scenarios. This helps in reinforcing the understanding of the material and gaining practical experience.
  • Some users feel that the teaching and training methods used in the course could be improved. They suggest incorporating more interactive elements, such as coding exercises or group discussions, to enhance the learning experience.
  • A few users have mentioned that the course heavily relies on reading slides and the accompanying book. They feel that more emphasis should be placed on active teaching methods, such as live coding or demonstrations, to make the content more engaging and easier to grasp.
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