2024
Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics
ICLR 2024spotlight
Hierarchical world models can significantly improve model-based reinforcement learning (MBRL) and planning by enabling reasoning across multiple time scales. Nonetheless, the majority of state-of-the-art MBRL methods employ flat, non-hierarchical models. We propose Temporal Hierarchies from Invarian…