Q-learning with temporal memory to navigate turbulence

TL;DR

This paper develops a reinforcement learning approach with temporal memory for odor-based navigation in turbulent environments, enabling agents to learn robust strategies similar to insect behavior.

Contribution

Introduces a novel RL algorithm with interpretable olfactory states and temporal memory for navigation without spatial perception in turbulent odor plumes.

Findings

Temporal memory improves navigation performance.

Optimal strategies involve cross-wind casting.

Robustness to environmental changes.

Abstract

We consider the problem of olfactory searches in a turbulent environment. We focus on agents that respond solely to odor stimuli, with no access to spatial perception nor prior information about the odor. We ask whether navigation to a target can be learned robustly within a sequential decision making framework. We develop a reinforcement learning algorithm using a small set of interpretable olfactory states and train it with realistic turbulent odor cues. By introducing a temporal memory, we demonstrate that two salient features of odor traces, discretized in few olfactory states, are sufficient to learn navigation in a realistic odor plume. Performance is dictated by the sparse nature of turbulent odors. An optimal memory exists which ignores blanks within the plume and activates a recovery strategy outside the plume. We obtain the best performance by letting agents learn their recovery strategy and show that it is mostly casting cross wind, similar to behavior observed in flying insects. The optimal strategy is robust to substantial changes in the odor plumes, suggesting minor parameter tuning may be sufficient to adapt to different environments.