Hieros: Hierarchical Imagination on Structured State Space Sequence World Models

TL;DR

Hieros introduces a hierarchical world model using S5 layers for efficient, multi-scale imagination in reinforcement learning, significantly improving sample efficiency, exploration, and prediction accuracy in Atari benchmarks.

Contribution

This paper presents Hieros, a novel hierarchical policy with S5-based world modeling that enhances imagination accuracy and efficiency over existing RNN and Transformer methods.

Findings

Outperforms state-of-the-art in Atari 100k benchmark

Predicts complex dynamics with high accuracy

Displays superior exploration capabilities

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 time scales in latent space. Hieros uses an S5 layer-based world model, which predicts next world states in parallel during training and iteratively during environment interaction. Due to the special properties of S5 layers, our method can train in parallel and predict next world states iteratively during imagination. This allows for more efficient training than RNN-based world models and more efficient imagination than Transformer-based world models. We show that our approach outperforms the state of the art in terms of mean and median normalized human score on the Atari 100k benchmark, and that our proposed world model is able to predict complex dynamics very accurately. We also show that Hieros displays superior exploration capabilities compared to existing approaches.