Practical_RL

Practical_RL: An Open Course on Reinforcement Learning

Practical_RL is a course designed to educate students on reinforcement learning in real-world scenarios. It is conducted both on-campus at prestigious institutions such as the Higher School of Economics (HSE) and the Yandex School of Data Analysis (YSDA), while also being structured to accommodate online students who can study in either English or Russian.

Course Philosophy:

• Curiosity-Driven Learning: The course is enriched with external resources for students who wish to delve deeper into topics that are not extensively covered in lectures. There are suggestions for further reading and bonus assignments designed for those eager to expand their knowledge.

• Focus on Practical Application: Rather than just theoretical concepts, this course emphasizes practical skills needed to tackle reinforcement learning challenges. It introduces students to practical tricks and heuristics with hands-on labs accompanying major ideas to reinforce learning through experience.

• Community Contributions: The course is a collaborative effort, and students are encouraged to contribute. If someone discovers a typo, finds an insightful resource, or improves code, they are welcome to make contributions through GitHub pull requests. This open-source approach enhances the learning materials continuously.

Course Structure:

FAQs and Technical Support:

To aid students, a comprehensive FAQ section and technical issues thread are maintained. Lecture slides and a survival guide for online students are also provided.

Learning Environment:

• Google Colab Integration: Students can easily access course materials via Google Colab, which is linked with the course's GitHub repository. This setup provides a convenient way to interact with notebooks and projects.
• Local Setup Recommendations: There are detailed guides on installing necessary dependencies on local machines, which is encouraged for personalized learning.
• Azure Notebooks as an Alternative: Students who prefer different platforms can choose Azure Notebooks for running course materials.

Course Syllabus:

The syllabus is structured to provide a gradual understanding of reinforcement learning, covering a wide array of topics with a mix of lectures and seminars:

1. Introduction to RL: Basics of decision processes and optimization.
2. Value-Based Methods: Techniques like value iteration and policy iteration.
3. Model-Free RL: Concepts like Q-learning and SARSA.
4. Deep Learning Recap: Covering fundamentals such as neural networks and PyTorch.
5. Approximate RL: Advanced topics like experience replay and DQNs.
6. Exploration Strategies: Exploration techniques including UCB and Thompson Sampling.
7. Policy Gradient Methods: Techniques like REINFORCE and Advantage Actor-Critic.
8. RL for Sequence Models: Tackling sequential data using RNNs and LSTMs.
9. Partial Observations (POMDP): Exploring learning and planning methods.
10. Advanced Policy-Based Methods: From trust-region policy optimization to deterministic gradient methods.
11. Model-Based RL: Emphasizing planning, imitation learning, and inverse RL.

This trajectory ensures a thorough grasp of reinforcement learning from fundamentals to advanced applications.

Course Team:

A dedicated team of educators and contributors, including Pavel Shvechikov, Alexander Fritsler, and several others, deliver lectures, seminars, and handle student assignments. They also oversee the course's technical and administrative needs.

Contributions:

The course content benefits from contributions by a vibrant community of developers and educators. Influential resources from esteemed sources like the Berkeley AI course are frequently referenced, and many assignments borrow elements from modern TensorFlow repositories.

In summary, Practical_RL represents an expertly curated course designed to offer a practical, community-focused approach to learning reinforcement learning that integrates theoretical foundations with experiential learning.