# A Biologically-Inspired Dual Stream World Model

arXiv: https://arxiv.org/abs/2209.08035 · HTML: https://mlsanity.com/paper/2209.08035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Successor Features for Transfer in Reinforcement Learning](https://mlsanity.com/paper/1606.05312/) (2016)
- [Neural Episodic Control](https://mlsanity.com/paper/1703.01988/) (2017)
- [Unsupervised Predictive Memory in a Goal-Directed Agent](https://mlsanity.com/paper/1803.10760/) (2018)
- [Generative Temporal Models with Spatial Memory for Partially Observed Environments](https://mlsanity.com/paper/1804.09401/) (2018)
- [Generative Temporal Models with Memory](https://mlsanity.com/paper/1702.04649/) (2017)

## Abstract (arXiv)

The medial temporal lobe (MTL), a brain region containing the hippocampus and nearby areas, is hypothesized to be an experience-construction system in mammals, supporting both recall and imagination of temporally-extended sequences of events. Such capabilities are also core to many recently proposed ``world models" in the field of AI research. Taking inspiration from this connection, we propose a novel variant, the Dual Stream World Model (DSWM), which learns from high-dimensional observations and dissociates them into context and content streams. DSWM can reliably generate imagined trajectories in novel 2D environments after only a single exposure, outperforming a standard world model. DSWM also learns latent representations which bear a strong resemblance to place cells found in the hippocampus. We show that this representation is useful as a reinforcement learning basis function, and that the generative model can be used to aid the policy learning process using Dyna-like updates.