• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.597-604
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• 2024

This study aims to develop a route-finding simulation focused on lead climbing, an official Olympic discipline. Objects were strategically using Unity placed and variables defined to construct a realistic climbing environment. Various climbing postures were generated, and the joint positions of the climber model were calculated using the FABRIK algorithm. ML-Agents were utilized to define vector observations and discrete actions, with stable postures determined using the CCW algorithm. Functions for behavior masking and agent action selection were implemented. Simulation results confirmed the feasibility of route-finding to the top hold, showing promise in enhancing training for lead climbers.