Evolutionary Algorithm for Parameter Optimization of Context-Steering Agents

Abstract

Context steering is a local approach to control an agent’s movement in a dynamically changing scene. Recent works have formalized the context-steering approach by Fray and presented a multiobjective view of the context-steering problem. Combining a variety of different behaviors, which can be used multiple times in different configurations for different context maps, introduces a large number of parameters that need to be tuned to obtain well-performing agents. This work aims to use evolutionary algorithms to optimize context-steering agents for various environments. A special focus lies on the evolution of agents that perform robustly across multiple variations of the same environment. To this end, we develop a real-valued encoding for a context-steering agent along with three different fitness functions to represent different goals of the agent. Our experimental evaluation shows that an evolutionary optimization can produce agent configurations that perform well with respect to different tasks and show a high intratask robustness. The proposed approach based on evolutionary optimization enables the user to optimize context-steering agents such that they can explore environments while avoiding dynamic obstacles.