dc.creator | Yanes Luis, Samuel | es |
dc.creator | Gutiérrez Reina, Daniel | es |
dc.creator | Toral, S. L. | es |
dc.date.accessioned | 2022-07-13T10:17:30Z | |
dc.date.available | 2022-07-13T10:17:30Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Yanes Luis, S., Gutiérrez Reina, D. y Toral, S.L. (2021). A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case. A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case, 9, 17084-17099. | |
dc.identifier.issn | 2169-3536 | es |
dc.identifier.uri | https://hdl.handle.net/11441/135301 | |
dc.description | Article number 9330612 | es |
dc.description.abstract | Autonomous surfaces vehicles (ASVs) excel at monitoring and measuring aquatic nutrients
due to their autonomy, mobility, and relatively low cost. When planning paths for such vehicles, the task
of patrolling with multiple agents is usually addressed with heuristics approaches, such as Reinforcement
Learning (RL), because of the complexity and high dimensionality of the problem. Not only do efficient paths
have to be designed, but addressing disturbances in movement or the battery’s performance is mandatory.
For this multiagent patrolling task, the proposed approach is based on a centralized Convolutional Deep
Q-Network, designed with a final independent dense layer for every agent to deal with scalability, with the
hypothesis/assumption that every agent has the same properties and capabilities. For this purpose, a tailored
reward function is created which penalizes illegal actions (such as collisions) and rewards visiting idle
cells (cells that remains unvisited for a long time). A comparison with various multiagent Reinforcement
Learning (MARL) algorithms has been done (Independent Q-Learning, Dueling Q-Network and multiagent
Double Deep Q-Learning) in a case-study scenario like the Ypacaraí lake in Asunción (Paraguay). The
training results in multiagent policy leads to an average improvement of 15% compared to lawn mower
trajectories and a 6% improvement over the IDQL for the case-study considered. When evaluating the
training speed, the proposed approach runs three times faster than the independent algorithm. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades (España) RTI2018-098964-B-I00 | es |
dc.description.sponsorship | Junta de Andalucía(España) PY18-RE0009 | es |
dc.format | application/pdf | es |
dc.format.extent | 16 p. | es |
dc.language.iso | eng | es |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | es |
dc.relation.ispartof | A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case, 9, 17084-17099. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep reinforcement learning | es |
dc.subject | Multiagent learning | es |
dc.subject | Monitoring | es |
dc.subject | Path planning | es |
dc.subject | Autonomous surface vehicle | es |
dc.subject | Patrolling | es |
dc.title | A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Electrónica | es |
dc.relation.projectID | RTI2018-098964-B-I00 | es |
dc.relation.projectID | US-1257508 | es |
dc.relation.projectID | PY18-RE0009 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/abstract/document/9330612 | es |
dc.identifier.doi | 10.1109/ACCESS.2021.3053348 | es |
dc.journaltitle | A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case | es |
dc.publication.volumen | 9 | es |
dc.publication.initialPage | 17084 | es |
dc.publication.endPage | 17099 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades | es |
dc.contributor.funder | Junta de Andalucía(España) | es |