Homing through Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning framework for adaptive homing and navigation in continuous 2D space, demonstrating optimal noise levels, inter-agent interactions, and improved efficiency over traditional active Brownian particles.

Contribution

The paper presents a novel RL-based model for homing that incorporates feedback, stochastic reorientation, and multi-agent interactions, advancing understanding of biological navigation mechanisms.

Findings

Optimal rotational diffusion level enhances homing efficiency.

Multi-agent interactions improve group navigation speed.

RL agents outperform ABPs in speed and trajectory stability.

Abstract

Homing and navigation are fundamental behaviors in biological systems that enable agents to reliably reach a target under uncertainty. We present a Reinforcement Learning (RL) framework to model adaptive homing in continuous two-dimensional domain. In this framework, the agent's state is given by its angular deviation from home, actions correspond to alignment or stochastic reorientation, and learning is driven by a radial-distance-based cost that penalizes motion away from the target. For a single self-propelled agent moving with constant speed, we find that the mean homing time $\langle T_{\mathrm{home}} \rangle$ exhibits a non-monotonic dependence on the rotational diffusion strength $D_r$, with an optimal noise level $D_r^{*}$, revealing a subtle interplay between exploration and goal-directed correction. Extending to two agents with soft repulsion, one agent consistently reaches home faster than the other, while in multi-agents system, repulsion ensures separation and the fastest agent becomes progressively faster as group size increases. Finally comparing the mean homing time $\langle T_{\mathrm{home}} \rangle$ of an Active Brownian Particle (ABP) and RL agent over an identical range of $D_r$, we find that RL trajectories are shorter, less noisy, and consistently faster. Our results show that cost-driven learning, stochastic reorientation, and inter-agent interactions enable efficient adaptive navigation, linking individual and collective homing. This reinforcement learning framework captures key biological features such as feedback-based route learning, randomness to escape unfavorable orientations, and mutual coordination.