[R] Hieros: Hierarchical Imagination on Structured State Space Sequence World Models

**OpenReview**: [https://openreview.net/forum?id=5j6wtOO6Fk](https://openreview.net/forum?id=5j6wtOO6Fk)

**arXiv**: [https://arxiv.org/abs/2310.05167](https://arxiv.org/abs/2310.05167)

**Code**: [https://github.com/Snagnar/Hieros](https://github.com/Snagnar/Hieros)

**Abstract**:

>One of the biggest challenges to modern deep reinforcement learning (DRL) algorithms is sample efficiency. Many approaches learn a world model in order to train an agent entirely in imagination, eliminating the need for direct environment interaction during training. However, these methods often suffer from either a lack of imagination accuracy, exploration capabilities, or runtime efficiency. We propose **Hieros**, a hierarchical policy that learns time abstracted world representations and imagines trajectories at multiple …

abstract accuracy agent algorithms capabilities challenges efficiency environment exploration imagination learn machinelearning modern reinforcement reinforcement learning sample train training world