Visual Sensor Network Reconfiguration with Deep Reinforcement Learning
We present an approach for reconfiguration of dynamic visual sensor networks with deep reinforcement learning (RL). Our RL agent uses a modified asynchronous advantage actor-critic framework and the recently proposed Relational Network module at the foundation of its network architecture. To address the issue of sample inefficiency in current approaches to model-free reinforcement learning, we train our system in an abstract simulation environment that represents inputs from a dynamic scene. Our system is validated using inputs from a real-world scenario and preexisting object detection and tracking algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement Learningobject-detectionObject Detectionreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
ReSPEC: A Framework for Online Multispectral Sensor Reconfiguration in Dynamic Environments
Multi-sensor fusion is central to robust robotic perception, yet most existing systems operate under static sensor configurations, collecting all modalities at fixed rates and fidelity regardless of their situational uti…
Reinforcement LearningCARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion
Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wid…
Autonomous VehiclesSensor FusionDominating Set Reconfiguration with Answer Set Programming
The dominating set reconfiguration problem is defined as determining, for a given dominating set problem and two among its feasible solutions, whether one is reachable from the other via a sequence of feasible solutions …
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration
Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The problem is typically solved by physical model-…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)GRL-SNAM: Geometric Reinforcement Learning with Path Differential Hamiltonians for Simultaneous Navigation and Mapping in Unknown Environments
We present GRL-SNAM, a geometric reinforcement learning framework for Simultaneous Navigation and Mapping(SNAM) in unknown environments. A SNAM problem is challenging as it needs to design hierarchical or joint policies …
Reinforcement Learning