Latent Action World Models for Control with Unlabeled Trajectories

This article introduces a research paper on using latent action world models for control tasks, specifically focusing on scenarios where trajectories are unlabeled. The core idea likely revolves around learning representations of actions and the environment from the observed data without explicit labels, which is a significant challenge in reinforcement learning and control.

• •Focuses on control tasks.
• •Utilizes latent action world models.
• •Addresses the challenge of unlabeled trajectories.