# Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents

arXiv: https://arxiv.org/abs/2106.15868 · HTML: https://mlsanity.com/paper/2106.15868/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Deep Reinforcement Learning from Self-Play in Imperfect-Information Games](https://mlsanity.com/paper/1603.01121/) (2016)
- [Hybrid Reward Architecture for Reinforcement Learning](https://mlsanity.com/paper/1706.04208/) (2017)

## Built on by (most cited first, 2 of 2)

- [Navigating Conceptual Space; A new take on Artificial General Intelligence](https://mlsanity.com/paper/2202.09646/) (2022)
- [Towards neoRL networks; the emergence of purposive graphs](https://mlsanity.com/paper/2202.12622/) (2022)

## Abstract (arXiv)

Navigating the world is a fundamental ability for any living entity. Accomplishing the same degree of freedom in technology has proven to be difficult. The brain is the only known mechanism capable of voluntary navigation, making neuroscience our best source of inspiration toward autonomy. Assuming that state representation is key, we explore the difference in how the brain and the machine represent the navigational state. Where Reinforcement Learning (RL) requires a monolithic state representation in accordance with the Markov property, Neural Representation of Euclidean Space (NRES) reflects navigational state via distributed activation patterns. We show how NRES-Oriented RL (neoRL) agents are possible before verifying our theoretical findings by experiments. Ultimately, neoRL agents are capable of behavior synthesis across state spaces -- allowing for decomposition of the problem into smaller spaces, alleviating the curse of dimensionality.