| Summary: | In this article, two generalized net models (GNMs) are proposed to study the behavior and effectiveness of intelligent virtual agents (IVA) working in a risky environment under different scenarios and training algorithms. The proposed GNMs allow for the selection of machine learning algorithms such as intensity of characteristics Q-learning (InCh-Q), as well as the modification of multi-plan reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), the generative adversarial imitation learning (GAIL) algorithm, and behavioral cloning (CB). The choice of action, the change in priorities, and the achievement of goals by the IVA are studied under different scenarios, such as fire extinguishing, rescue operations, evacuation, patrolling, and training. Transitions in the GNMs represent the scenarios and learning algorithms. The tokens that pass through the GNMs can be the GNMs of the IVA architecture or the IVA memory model, which are enriched with knowledge and experience during the experiments, as the scenarios develop. The proposed GNMs are formally correct and, at the same time, understandable, practically applicable, and convenient for interpretation. Achieving GNMs that meet these requirements is a complex problem. Therefore, issues related to the design and use of GNMs for the reliable modeling and analysis of the behavior and effectiveness of IVAs operating in a dynamic and risky environment are discussed. Some advantages and challenges in using GNMs compared to other classical models used to study IVA behavior are considered.
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