The purpose of the credit module is to form in applicants for the third level of higher education (PhD) the abilities of a systematic scientific worldview, general cultural outlook and competencies to identify, pose and solve research problems in the field of computer science, evaluate and ensure the quality of research performed. In particular, to learn both the fundamental principles of the theory of step-by-step decision-making (the theory of Markov decision-making processes) and dynamic programming, and be able to apply the acquired theoretical knowledge to solve applied, in particular, problems of making optimal decisions in industry (technical support of industrial systems, industrial safety examination system); robotics (automated forecasting); business (marketing, inventory management); computer science (troubleshooting networks, optimizing requests to distributed database servers); state security and military sciences (search for moving targets, target identification, distribution of weapons); health care (medical diagnostics, development of treatment protocols), as well as postgraduate students must master the following competencies: general - GC 4 Ability to independently conduct research activities, including analysis of problems, setting goals and objectives, selection of means and methods of research, as well as assessment of its quality; GC 5 Ability to initiate, plan, implement and adjust a sequential process of thorough scientific research; GC 6 Ability to critically analyze, evaluate, and synthesize new and complex ideas; GC 7 Ability for continuous self-development and self-improvement; professional – FC 1 Ability to initiate complex projects using a systematic approach and implement them independently; FC 2 Ability to comply with moral and ethical rules of conduct, research ethics, characteristics for participants in the academic environment, as well as the rules of academic integrity in scientific research; FC 3 Ability to critically analyze the positive and negative qualities of existing methods of system analysis, as well as to assess their capabilities for further use in solving specific scientific and practical problems; FC 4 Ability to make scientifically sound decisions in conditions of uncertainty, which requires the development of new methods and the conduct of research and innovation activities; FC 5 Ability to carry out research and professional activities at an interdisciplinary level; FC 6 Ability to deeply analyze and create new methods for analyzing data and knowledge; FC 7 Ability to perform research on loosely structured problems, develop new methods, and then solve them; FC 8 Ability to plan and conduct scientific research, prepare, present and publish the results of research activities. Upon completion of the course, applicants for the third level of higher education should acquire the following program learning outcomes: PRN 4 Know the advantages and disadvantages of existing methods of system analysis and the possibility of their use to solve specific scientific and applied problems in intelligent decision support systems; PRN 5 Know the basics of the organization of the research scientific process to solve significant problems in the field of system analysis, be able to apply knowledge of the basics of analysis and synthesis in various subject areas, critical comprehension and solution of research problems; PRN 10 Be able to create new methods of system analysis and mathematical models of complex systems of various nature; PRN 11 Be able to develop and use new methods for analyzing complex systems and new methods of decision-making under uncertainty; PRN 12 Be able to critically analyze the advantages and disadvantages of known methods of system analysis, as well as be able to assess the possibilities of their use to solve specific scientific and practical problems; PRN 13 Be able to develop scientific projects in the field of system analysis; PRN 14 Be able to implement the results of scientific research based on the methods of system analysis; PRN 15 Be able to solve complex problems in the field of system analysis or as a result of research and innovation activities, which involves a deep rethinking of existing and the creation of new holistic knowledge; PRN 17 Read and understand foreign texts in the specialty; freely present and discuss with specialists and non-specialists the results of research, scientific and applied problems of the industry in the state and foreign languages, competently reflect the results of research in scientific publications in leading international scientific journals; PRN 18 Adhere to the rules of academic integrity; know and adhere to the basic principles of academic integrity in scientific and educational (pedagogical) activities. Subject of study. Tasks and classes of reinforcement learning methods are just like the area of knowledge that includes the tasks of step-by-step optimal decision-making with partial observations The main tasks of the credit module. According to the requirements of the program of the discipline, postgraduate students after mastering the credit module must demonstrate the following learning outcomes: Knowledge: methods and means of reinforcement learning. Skills: solve real-world problems using reinforcement learning methods and algorithms. In particular, to formalize the problem of step-by-step optimal decision-making as a partially observable Markov decision-making process with possibly unknown transient probabilities and rewards, to apply modern algorithms for approximate solution of such problems, the ability to use relevant information technologies and create their own software products to solve real problems making optimal decisions in industry (technical support of industrial systems, industrial safety examination system); robotics (automated forecasting); business (marketing, inventory management); computer science (troubleshooting networks, optimizing requests to distributed database servers); state security and military sciences (search for moving targets, target identification, distribution of weapons); health care (medical diagnostics, development of treatment protocols). Experience: creation of a research laboratory for reinforcement learning (a paradigm of organized collaboration based on the experience of leading national laboratories in the United States), where the role of each team member is to specialize in a particular task in order to become the best at it, while having a holistic view of the entire process.