Application of Reinforcement Learning and Parallel Programming Technologies for Program Code Generation and Validation

Abstract

At the moment, neural networks are able to create what was previously considered inaccessible: photorealistic faces of people; full-fledged paintings based on rough sketches; any images with a short text description; poems and prose on the first lines or a given topic. All of this has been made possible by rapid advances in areas such as natural language processing and machine vision. Neural networks are capable of generating content based on the data they have memorized during an extensive learning process. Problems for logic, mathematics and logical reasoning are an example of flexible intelligence, and it requires completely different approaches to learning. The study presented in the article proposes the development of a methodology for designing and training neural networks aimed at creating a functioning code. The basis of the study is the possibility of using artificial intelligence, in particular neural networks, to generate code by a machine, that is, AI4Code tasks. The study examined the provisions in favor of the use of reinforcement learning in comparison with language models, as well as the architecture of the environment necessary for such learning. The main research is the focus on the use of Nvidia graphics accelerators and the use of central processes of various architectures. The article discusses the features of creating a learning environment, the advantages and disadvantages of the CUDA platform, and analyzes the potential effectiveness of each of the approaches.

Keywords

• reinforcement learning
• program code
• neural networks
• learning environment
• learning platforms