The introduction of Supervised Contrastive Learning into Imitation Learning aims to improve how agents understand and react within video game environments. By creating latent representations that capture action-relevant factors, this method seeks to strengthen the cause-effect relationships from observations to actions. This means that if a player jumps at an obstacle, the system better recognizes and anticipates such actions based on the corresponding environment cues.
Experiments conducted on both 3D games like Astro Bot and Returnal, as well as various 2D Atari games, reveal that this integration results in improved representation quality and enhanced learning processes. The approach also demonstrates faster convergence and better generalization capabilities compared to traditional models that rely solely on supervised action prediction loss functions. The implications of these advancements could lead to more intelligent game agents and more immersive gaming experiences.
👉 Pročitaj original: arXiv AI Papers
