Decomposing the Prediction Problem; Autonomous Navigation by neoRL Agents

TL;DR

This paper introduces neoRL, a neural representation-based reinforcement learning approach inspired by neuroscience, enabling autonomous navigation by decomposing the problem into smaller, manageable state spaces.

Contribution

The paper presents a novel neoRL framework that leverages neural representations for state decomposition, improving navigation in high-dimensional spaces.

Findings

neoRL enables behavior synthesis across decomposed state spaces

Decomposition alleviates the curse of dimensionality in navigation tasks

Theoretical and experimental validation of neoRL's effectiveness

Abstract

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.