COMPARISON OF MACHINE LEARNING METHODS AND TRADITIONAL ALGORITHMS IN NPC NAVIGATION AND BEHAVIOR TASKS

Abstract

As part of this study, the Unity ML-Agents toolkit, which implements the reinforcement learning paradigm, was tested as a methodological foundation for modeling the autonomous behavior of non-player characters (NPCs). This approach is considered a conceptually distinct and adaptive alternative to classical control architectures, particularly behavior trees and state machines, as it provides a higher level of nonlinearity, variability, and cognitive complexity in agent behavior.
The empirical part of the study was implemented in the Unity environment using the game “Snake” as a test case for comparative analysis. The study compares the performance of NPCs controlled by traditional algorithms with the results obtained using ML-Agents that employed Proximal Policy Optimization (PPO) and its modification with Long Short-Term Memory (LSTM) networks (PPO+LSTM). Comparative tables of results allow for a quantitative and qualitative assessment of the effectiveness of the proposed approach, as well as an outline of its potential in the context of further research in the field of intelligent agent systems.
The scientific novelty of this study lies in the verification of the hypothesis regarding the feasibility of using reinforcement learning methods as an alternative to traditional control architectures, as confirmed by empirical results obtained in the test environment of the “Snake” game. The practical significance of this work lies in the fact that the proposed recommendations and the results of the comparative analysis can be directly integrated into the development process of modern game products, contributing to an increase in the level of autonomy, adaptability, and intellectual complexity of NPC behavior.
The conclusions of this work formulate a set of practical recommendations addressed to game developers and game designers who are seeking alternative approaches to organizing and controlling the behavior of non-player characters in their own projects. In addition, a systematic comparative analysis was conducted of the integration capabilities and limitations of various architectural solutions, specifically the Unity ML-Agents toolkit and behavior trees (Behavior Trees), which allows not only to outline their functional advantages and disadvantages in the context of practical application but also to identify promising directions for the further optimization of agent-based systems in the field of interactive digital environments.
Thus, this study not only expands theoretical understanding of the potential applications of policy optimization algorithms in the field of interactive simulations, but also lays the groundwork for future research aimed at developing scalable models of agent behavior capable of achieving a qualitatively new level of cognitive adaptability in digital environments.