Providing Rapid Design Feedback for 3D Obstacle Course Games Using Constrained Solvability Queries

2026-09-28

AI Summary

This study addresses the lack of rapidly verifiable solution feedback in 3D obstacle course level design by proposing an interactive system design framework that integrates constraint querying with a GPU-accelerated simulator. The approach pioneers the fusion of constraint solving with the GoExplore search algorithm, offline reinforcement learning agents, and a custom GPU simulator to establish an efficient design loop for evaluating level feasibility and difficulty. Achieving a simulation speed 14,000 times faster than real time, the system enables near-instantaneous playability assessment. User studies confirm that the framework effectively guides intended gameplay behaviors, inspires novel design directions, and significantly enhances both iterative efficiency and design insight.

Abstract

We present a system that aids the design of 3D obstacle course games by providing designers with rapid feedback on how obstacles can be solved. Our core contribution is a system for querying for solutions (sequences of player actions) to an obstacle that adhere to designer-specified constraints (e.g., avoid a region, travel through a given waypoint, only perform two jumps, etc.). To solve a wide range of obstacle designs quickly, we author a high-performance implementation of the GoExplore algorithm for exploratory search, and guide search with an obstacle solving agent trained offline using reinforcement learning (RL). To further accelerate search, the system carries out exploration using a custom GPU-accelerated obstacle course game simulator that generates playthrough experience at nearly 14,000$\times$ real time, 60-fps gameplay. Through design studies, we demonstrate that the use of constrained solvability queries in a rapid design loop is sufficiently expressive to help designers understand ways an obstacle can be solved or why it cannot be solved. We also show how the tool can let designers answer higher-level questions such as identifying undesirable solution paths and assessing the difficulty of solutions. This allows them to pursue new design directions they did not originally anticipate. Human playtesting of obstacles designed using our system confirms that human players indeed play the obstacles in the manner the designers intended. We release code for our interactive tool, simulator, training setup, and procedural level generation system at https://zandermajercik.github.io/interactive-obstacle-course-feedback/.

Problem

• 3D obstacle course games
• rapid design feedback
• constrained solvability
• game design
• playtesting

Innovation

• Constrained Solvability Queries
• GoExplore Algorithm
• Reinforcement Learning
• GPU-accelerated Simulation
• 3D Obstacle Course Games