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Education around the world is constantly developing and improving, teaching standards are changing, the content of education is changing, and the requirements for a modern teacher are increasing. The Federal State Educational Standard puts at the forefront new approaches and requirements for the educational system as a whole and for each individual subject in particular. The introduction of extracurricular activities, design technologies, an innovative approach poses new challenges for the entire education system and for the modern teacher, the school and the education system itself is becoming more and more innovative in development. The development of hardware and software, the increasing introduction of network communications into the educational process leads to the introduction of innovative technologies. The implementation of which is now the most optimal on the basis of actively developing and widespread information technologies. The teacher receives an excellent tool and teaching tool, which, in skillful hands, allows you to change the approaches to the transfer of the studied material, test instruments and change the emotional and psychological climate in the lesson. The article examines various innovative technologies and the possibilities of their adaptation to the use of modern teaching aids, highlights the main options for the digital configuration of the information educational environment for organizing the use of innovative technologies in physics lessons, offers methodological recommendations for conducting physics lessons and using innovative technologies to increase the motivation of learning. The greatest attention is paid to the technologies that were revealed in the results of the questionnaire survey of physics teachers and the use of which helps to increase the motivation of students to study physics, such as game technologies; portfolio technology; method of projects. Examples of innovative developments for physics lessons have been developed, aimed at increasing the motivation of teaching physics by means of innovative technologies and using information technologies.

Increasing the profitability of sales agents has been and continues to be given great attention. The article discusses various approaches to solving this problem, one of which is reinforcement learning, which is actively used to solve algorithmic trading problems. Increasing the efficiency (profitability) of algorithmic trading is possible in two ways, on the one hand, improving the algorithms, on the other hand, enriching the data that is transmitted to the input to the algorithms. The study confirmed the feasibility of using derived financial indicators for the tasks of applying trading algorithms based on reinforcement learning algorithms. The main idea of ​​the research implementation is aimed at obtaining the results of the sales agent's work based on Q-learning on technical indicators and on derived technical indicators (the agent is implemented in Python). The substantiation of the choice of the Q-learning method for solving the problem is carried out, the basics of decision-making, policy, strategy, and reinforcement learning are considered. The paper considers the issues of increasing the efficiency (profitability) of a sales agent based on the Q-learning algorithm by transferring derivative technical indicators to him, determined and substantiated derived technical indicators, verified the results of a sales agent's work on technical indicators and derived technical indicators. In the study presented in the article, an empirical test of the possibility of creating synthetic financial features to improve the efficiency of learning algorithms was carried out, in addition, the verification of obtaining the necessary results when using reinforcement learning algorithms was carried out. Empirical confirmation has been carried out that the use of derived financial indicators to increase the efficiency (profitability) of sales agents based on the Q-learning method with the use of reinforcement learning algorithms is expedient.

At the moment, there is a large volume of literature on exchange trading. Obviously, every year the mathematical base of work is becoming more complicated along with an increase in computing power, machines can process more metrics from year to year and produce more accurate solutions per unit of time. The use of deep learning has already proven itself well, as the application of this approach has given a quantum leap in algorithmic trading. The article presents an algorithm for trading long contracts with one asset in the financial market in the Python programming language using the LSTM neural network using the Keras library, which is used as a demo example in the Reinforcement Learning discipline. The formalized LSTM model solves the vanishing gradient problem, which can hold the gradient of the objective function relative to the state signal. As applied to our problem, such an improvement in the model allows us to collect data on certain patterns of price changes, that is, when predicting the price of the next step, we rely not only on the data of the previous step, but also on earlier data, when there was a similar state of the environment. Sharpe Ratio is used to determine the optimal strategy and make decisions at each time of application. The optimal minimum time period for the model operation has been determined; the signal transmission delay from the moment the market situation changes until the signal is received by the model, which will be infinitely small, and the computing power will be considered infinitely large. These assumptions give the right to say: when the market situation changes, the model is instantly ready to react and make a decision to sell, buy or hold an asset.

Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.