Deep reinforcement learning (DRL) is currently the most popular AI-based approach to autonomous vehicle control. An agent, trained for this purpose in simulation, can interact with the real environment with a human-level performance. Despite very good results in terms of selected metrics, this approach has some significant drawbacks: high computational requirements and low explainability. Because of that, a DRL-based agent cannot be used in some control tasks, especially when safety is the key issue. Therefore we propose to use Tangled Program Graphs (TPGs) as an alternative for deep reinforcement learning in control-related tasks. In this approach, input signals are processed by simple programs that are combined in a graph structure. As a result, TPGs are less computationally demanding and their actions can be explained based on the graph structure. In this paper, we present our studies on the use of TPGs as an alternative for DRL in control-related tasks. In particular, we consider the problem of navigating an unmanned aerial vehicle (UAV) through the unknown environment based solely on the on-board LiDAR sensor. The results of our work show promising prospects for the use of TPGs in control related-tasks.

A standard class of approaches in applying machine learning to code is to construct a graph representation of a program, and then to perform the analysis of interest on that graph representation, learning from a large dataset of labeled example programs. Graph representations of programs used for machine learning include the abstract syntax tree (AST), control-flow graph (CFG), data-flow graphs, inter-procedural control-flow graph (ICFG), interval graph, and composite "program graphs" that encode information from multiple of the aforementioned graphs, possibly with additional program-derived data. The python_graphs library directly allows for the construction of some of these graph types (e.g., control-flow graphs and composite program graphs) from arbitrary Python programs, and it provides tools that aid in constructing the others. It has been used successfully in a variety of machine learning for code publications, and we make it available as free and open source software to allow for broader use. In Section 2 we present an overview of the use of graph representations of code in machine learning. In Section 3 we describe the capabilities (Section 3.1), possible extensions (Section 3.2), and limitations (Section 3.3) of python_graphs. Section 4 highlights the applications of python_graphs for machine learning research. Section 5 presents a case study applying python_graphs to 3.3 million programs from Project CodeNet [28].

We present a multi-agent system where agents can cooperate to solve a system of dependent tasks, with agents having the capability to explore a solution space, make inferences, as well as query for information under a limited budget. Re-exploration of the solution space takes place by an agent when an older solution expires and is thus able to adapt to dynamic changes in the environment. We investigate the effects of task dependencies, with highly-dependent graph $G_{40}$ (a well-known program graph that contains $40$ highly interlinked nodes, each representing a task) and less-dependent graphs $G_{18}$ (a program graph that contains $18$ tasks with fewer links), increasing the speed of the agents and the complexity of the problem space and the query budgets available to agents. Specifically, we evaluate trade-offs between the agent's speed and query budget. During the experiments, we observed that increasing the speed of a single agent improves the system performance to a certain point only, and increasing the number of faster agents may not improve the system performance due to task dependencies. Favoring faster agents during budget allocation enhances the system performance, in line with the "Matthew effect." We also observe that allocating more budget to a faster agent gives better performance for a less-dependent system, but increasing the number of faster agents gives a better performance for a highly-dependent system.

Neural Module Network (NMN) is a machine learning model for solving the visual question answering tasks. NMN uses programs to encode modules' structures, and its modularized architecture enables it to solve logical problems more reasonably. However, because of the non-differentiable procedure of module selection, NMN is hard to be trained end-to-end. To overcome this problem, existing work either included ground-truth program into training data or applied reinforcement learning to explore the program. However, both of these methods still have weaknesses. In consideration of this, we proposed a new learning framework for NMN. Graph-based Heuristic Search is the algorithm we proposed to discover the optimal program through a heuristic search on the data structure named Program Graph. Our experiments on FigureQA and CLEVR dataset show that our methods can realize the training of NMN without ground-truth programs and achieve superior efficiency over existing reinforcement learning methods in program exploration.

Over the last five years, deep learning-based methods have revolutionised a wide range of applications, for example those requiring understanding of pictures, speech and natural language. For computer scientists, a naturally arising question is whether computers learn to understand source code? It appears to be a trivial question at first glance because programming languages indeed are designed to be understood by computers. However, many software bugs are in fact instances of Do what I mean, not what I wrote. In other words, small typos can have big consequences.

Graphs belong to the field of mathematics, graph theory. For data analysis that requires searches of particular patterns, graph-based data mining becomes an important technique. Indeed, in real life, most of the data we have to deal with can be represented as graphs. A typical graph consists of vertices (nodes, cells), and of edges that are the connecting lines between the nodes. Analysis of graphs includes determining certain details about the graph structure.