Emergence of a Flow-Assisted Casting Strategy for Olfactory Navigation via Memory-Augmented Reinforcement Learning

• URL: http://arxiv.org/abs/2605.18881v1
• Date: Sat, 16 May 2026 03:32:48 GMT
• Title: Emergence of a Flow-Assisted Casting Strategy for Olfactory Navigation via Memory-Augmented Reinforcement Learning
• Abstract summary: We investigate the navigation performance of Reinforcement Learning (RL) agents in unsteady flows under varying memory lengths and flow conditions.<n>The average speed toward the odor source exhibits a non-monotonic dependence on memory length, which can be explained by the "sector-search" model.
• Score: 0.1749935196721634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In dynamic flow fields, various animals exhibit remarkable odor search capabilities despite relying on stochastic detections. Interestingly, there exists an optimal time window for integrating these detections that maximizes search efficiency. To understand the underlying mechanism, we investigate the navigation performance of Reinforcement Learning (RL) agents in unsteady flows under varying memory lengths and flow conditions. Without any predefined models, the agents develop a flow-assisted casting strategy and adaptively adjust both the geometry of their search trajectories and the concentration threshold for initiating casting to maximize the success rate. The agent's average speed toward the odor source exhibits a non-monotonic dependence on memory length, which can be explained by the "sector-search" model.

Related papers

• Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning [0.815557531820863]
  We present a reinforcement learning approach to train autonomous agents to reach arbitrary targets in a chaotic double-gyre flow.<n>We analyze the impact of providing agents with explicit global flow parameters.<n>The experiments reveal a trade-off in sensor utility: velocity-aware agents optimize energy efficiency, whereas vorticity sensors provide superior structural mapping and achieve better target proximity.
  arXiv  Detail & Related papers  (2026-07-15T07:57:26Z)
• Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery [0.6393971049475565]
  We use Q-learning to train an olfactory search agent with a minimal memory of past observations.<n>This agent learns an interpretable strategy to recover the plume which combines well-known behaviors observed in insects.
  arXiv  Detail & Related papers  (2026-05-15T13:19:26Z)
• Olfactory pursuit: catching a moving odor source in complex flows [0.5541644538483947]
  Odor signals are intermittent, strongly mixed by turbulent-like transport, and typically lag behind the true target position.<n>We formulate olfactory pursuit as a partially observable Markov decision process in which an agent maintains a joint belief over the target's position and velocity.<n>Our results identify predictive inference of target motion as the key ingredient for effective olfactory pursuit.
  arXiv  Detail & Related papers  (2026-04-13T15:52:24Z)
• Predictive Modeling in AUV Navigation: A Perspective from Kalman Filtering [2.646393510325155]
  We present a framework for autonomous underwater vehicles (AUVs) that improves localization accuracy, enhances trajectory prediction, and supports efficient search operations during communication loss.<n>Our framework fuses multi-buoy acoustic data with Kalman filtering and uncertainty propagation to maintain navigation accuracy and yield robust search-region definitions during communication loss.
  arXiv  Detail & Related papers  (2026-03-28T21:47:22Z)
• DLLM Agent: See Farther, Run Faster [94.74432470237817]
  Diffusion large language models (DLLMs) have emerged as an alternative to autoregressive (AR) decoding with appealing efficiency and modeling properties.<n>We study this in a controlled setting by instantiatingDLLM and AR backbones within the same agent workflow.<n>We find thatDLLM Agents are on average over 30% faster end to end than AR agents, with some cases exceeding 8x speedup.
  arXiv  Detail & Related papers  (2026-02-07T09:01:18Z)
• FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers [51.56484100374058]
  FlowLPS solves inverse problems with pretrained flow models via a Langevin Proximal Sampling (LPS) strategy.<n>Our method integrates Langevin dynamics for manifold-consistent exploration with proximal optimization for precise mode seeking, achieving a superior balance between reconstruction fidelity and perceptual quality.
  arXiv  Detail & Related papers  (2025-12-08T04:18:13Z)
• Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning [73.40364018029673]
  Continual test-time adaptive object detection (CTTA-OD) aims to online adapt a source pre-trained detector to ever-changing environments.<n>Our motivation stems from the observation that not all learned source features are beneficial.<n>Our method achieves superior adaptation performance while reducing computational overhead by 12% in FLOPs.
  arXiv  Detail & Related papers  (2025-06-03T05:27:56Z)
• AutoTurb: Using Large Language Models for Automatic Algebraic Model Discovery of Turbulence Closure [15.905369652489505]
  In this work, a novel framework using LLMs to automatically discover expressions for correcting the Reynolds stress model is proposed. The proposed method is performed for separated flow over periodic hills at Re = 10,595. It is demonstrated that the corrective RANS can improve the prediction for both the Reynolds stress and mean velocity fields.
  arXiv  Detail & Related papers  (2024-10-14T16:06:35Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [81.93945602120453]
  We introduce an approach that is both general and parameter-efficient for face forgery detection.<n>We design a forgery-style mixture formulation that augments the diversity of forgery source domains.<n>We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration [15.463313629574111]
  This paper investigates how to achieve sample-efficient exploration in continuous control tasks. We introduce an RL algorithm that incorporates a predictive model and off-policy learning elements. We derive an intrinsic reward without incurring parameters overhead.
  arXiv  Detail & Related papers  (2024-03-31T11:39:11Z)
• Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization [87.21285093582446]
  Diffusion Generative Flow Samplers (DGFS) is a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments. Our method takes inspiration from the theory developed for generative flow networks (GFlowNets)
  arXiv  Detail & Related papers  (2023-10-04T09:39:05Z)
• Generative Flows with Invertible Attentions [135.23766216657745]
  We introduce two types of invertible attention mechanisms for generative flow models. We exploit split-based attention mechanisms to learn the attention weights and input representations on every two splits of flow feature maps. Our method provides invertible attention modules with tractable Jacobian determinants, enabling seamless integration of it at any positions of the flow-based models.
  arXiv  Detail & Related papers  (2021-06-07T20:43:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.