A transformer-based deep reinforcement learning approach to spatial navigation in a partially observable Morris Water Maze

TL;DR

This paper introduces a novel transformer-based deep reinforcement learning method for spatial navigation in a 2D Morris Water Maze, demonstrating improved decision-making in partially observable environments and mimicking biological navigation strategies.

Contribution

It is the first to apply a transformer-based deep Q-network to spatial navigation in the Morris Water Maze, showcasing its effectiveness in partially observable settings.

Findings

Transformer architecture enables efficient learning of navigation strategies.

The approach overcomes limited visual information challenges.

Potential for understanding artificial agents' internal representations.

Abstract

Navigation is a fundamental cognitive skill extensively studied in neuroscientific experiments and has lately gained substantial interest in artificial intelligence research. Recreating the task solved by rodents in the well-established Morris Water Maze (MWM) experiment, this work applies a transformer-based architecture using deep reinforcement learning -- an approach previously unexplored in this context -- to navigate a 2D version of the maze. Specifically, the agent leverages a decoder-only transformer architecture serving as a deep Q-network performing effective decision making in the partially observable environment. We demonstrate that the proposed architecture enables the agent to efficiently learn spatial navigation strategies, overcoming challenges associated with a limited field of vision, corresponding to the visual information available to a rodent in the MWM. Demonstrating the potential of transformer-based models for enhancing navigation performance in partially observable environments, this work suggests promising avenues for future research in artificial agents whose behavior resembles that of biological agents. Finally, the flexibility of the transformer architecture in supporting varying input sequence lengths opens opportunities for gaining increased understanding of the artificial agent's inner representation of the environment.