Bootcamp on Reinforcement Learning and Research is an intensive practice-oriented course for those who want to thoroughly master Reinforcement Learning (RL): from mathematical foundations and classic algorithms to the development of intelligent agents, RLHF, Agentic RAG, and preparing their own research project.
About the Course
The program helps participants progress consistently from the basic principles of reinforcement learning to modern research approaches in AI. You will learn how an agent interacts with the environment, receives rewards, optimizes its behavior strategy, and learns to make decisions under complex conditions.
Particular emphasis is placed on practice: participants will implement RL algorithms, train agents for gaming and applied environments, work with modern frameworks, and apply reinforcement learning methods to improve AI systems and LLM applications.
What You Will Learn
- Understand the fundamental principles of reinforcement learning: agent, environment, state, action, reward, policy, and value function.
- Formalize RL tasks through Markov Decision Process (MDP) and choose appropriate solution methods.
- Implement key algorithms: Dynamic Programming, Monte Carlo, and Temporal Difference Learning.
- Create and train intelligent agents capable of solving complex gaming and simulation tasks.
- Use Policy Gradient methods for optimizing agent behavior strategies.
- Understand RLHF (Reinforcement Learning from Human Feedback) approaches and apply them for fine-tuning models.
- Work with Group Relative Policy Optimization (GRPO) and modern policy optimization techniques.
- Design Agentic RAG applications, where agents use retrieval, tools, and feedback to improve outcomes.
- Conduct experiments, analyze metrics, formulate hypotheses, and prepare a research project for publication.
Bootcamp Program
Fundamentals of Reinforcement Learning
You will study the basic concepts of RL and learn to describe decision-making tasks in terms of states, actions, rewards, and policies. This block forms the foundation for understanding all subsequent algorithms.
Classic Reinforcement Learning Algorithms
This section covers methods like Dynamic Programming, Monte Carlo, and Temporal Difference Learning. You'll understand how value estimation works, policy improvement, and learning from interaction with the environment.
Deep Reinforcement Learning
Participants move from table-based methods to neural approaches and learn to apply deep learning for building agents working in more complex and high-dimensional environments.
Policy Gradient and Modern Optimization Methods
You will be introduced to direct policy optimization methods, including Policy Gradient and GRPO. These approaches are especially important for modern AI systems that require fine-tuning of model behavior.
RLHF and Model Training with Human Feedback
A separate module is dedicated to Reinforcement Learning from Human Feedback. You will understand how human preferences are used to improve the quality of model responses and tailor their behavior to real user tasks.
Agentic RAG and Applied AI Systems
You will learn to design agent applications, combining retrieval, generative models, tools, and feedback mechanisms. This approach is used to create more reliable and useful AI assistants.
Research Project
The final part of the bootcamp is dedicated to independent research work. You will formulate a hypothesis, conduct experiments, analyze results, and prepare a draft article suitable for further refinement and submission to a scientific conference.
Who This Course Is For
- For ML and DL engineers who want to delve into Reinforcement Learning and modern agent training methods.
- For researchers and students planning to work on scientific projects in RL, LLM, and agent systems.
- For AI product developers who need to understand how to apply RLHF, Policy Optimization, and Agentic RAG in practice.
- For specialists looking to transition from basic machine learning to more advanced areas of artificial intelligence.
Participant Requirements
- Confident proficiency in Python and basic development tools.
- Understanding of the basics of Machine Learning and Deep Learning.
- Experience working with PyTorch or similar deep learning frameworks.
- Basic knowledge of probability theory, mathematical analysis, and linear algebra.
- Willingness to read research materials, conduct experiments, and analyze results.
Practical Outcomes After Completion
- Own portfolio of projects in reinforcement learning.
- Implemented RL algorithms and trained intelligent agents.
- Understanding of modern approaches to RLHF, GRPO, and policy optimization.
- Experience in developing Agentic RAG applications and AI systems with autonomous behavior elements.
- Prepared research project or draft article for further publication.
Why You Should Attend This Bootcamp
Reinforcement learning remains one of the key areas of artificial intelligence development. It is used in robotics, gaming systems, autonomous agents, optimization, LLM, recommendation systems, and research AI projects.
This bootcamp combines theory, engineering practice, and research approach. As a result, you will not only become familiar with algorithms but also learn to apply them to create real intelligent systems and conduct independent experiments.
