EPC2024 | Reinforcement Learning for NPC Locomotion in Physics-Based Worlds | Ali Ghadirzadeh

The talk explores the application of reinforcement learning (RL) to enhance non-player characters (NPCs) locomotion within physics-based virtual worlds. By leveraging RL, NPCs can autonomously adapt and improve their locomotion strategies, navigating complex terrains and interacting with obstacles in a physically plausible manner.
The discussion delves into the implementation of RL algorithms for NPC behavior learning and addresses challenges associated with learning diverse locomotion gaits using RL across various characters. Through case studies and demonstrations, we highlight how RL-driven locomotion enhances NPC realism, responsiveness, and adaptability in virtual worlds, offering a path towards more immersive gaming experiences.