Course contents: The course introduces students to the concept of autonomous agents—entities that perceive their environment, think, and act based on observations and goals. Emphasis is placed on the practical implementation of agents using reinforcement learning (RL) techniques and modern technologies. The course covers both theoretical principles and practical applications across various environments, including web agents, video games, and robotics, familiarizing students with development tools and experimental frameworks.

At the end of the course the student will be able to:

• Describe the theoretical framework and core components of autonomous agents.
• Analyze the Observation–Thought–Action cycle across different environments.
• Implement agents using reinforcement learning and key RL algorithms such as Q-Learning and PPO.
• Utilize modern tools and development environments for agent training.
• Integrate Large Language Models (LLMs) and APIs into agent architectures for complex tasks.

Assessment: Course evaluation is based on short assignments and/or a midterm progress assessment during the semester, with a total weight of up to 40%. The final semester project (code and technical report) accounts for 60%. These percentages may vary (by up to ±10%) from year to year. To pass the course, students must achieve a passing grade both in the final project and in the overall grade. The assignments may be accompanied by an oral examination.