Fast Neural-Network Approximation of Active Target Search Under Uncertainty

arXiv:2604.22254v1 Announce Type: new Abstract: We address the problem of searching for an unknown number of stationary targets at unknown positions with a mobile agent. A probability hypothesis density filter is used to estimate the expected number of targets under measurement uncertainty. Existing planners, such as Active Search (AS) and its Intermittent variant (ASI), achieve accurate detection but require costly online optimization. To reduce online computation, we propose to use a convolutional neural network to approximate AS or ASI decisions through direct inference. The network is trained on AS/ASI data using a multi-channel grid that encodes target beliefs, the agent position, visitation history, and boundary information. Simulations with uniform and clustered target distributions show that the network achieves detection rates comparable to AS or ASI while reducing computation by orders of magnitude.

Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication
Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning
Event-Driven Temporal Graph Networks for Asynchronous Multi-Agent Cyber Defense in NetForge_RL
Alleviating Community Fear in Disasters via Multi-Agent Actor-Critic Reinforcement Learning

Source: arXiv cs.LG | 2026-04-27

Related