# Comprehensive Ocean Information-Enabled AUV Path Planning Via Reinforcement Learning

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/comprehensive-ocean-information-enabled-auv-path-planning-via-reinforcement/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Meng Xi,Jiachen Yang,Jiabao Wen,Hankai Liu,Yang Li,Houbing Song |
| Citations | 131 |
| DOI | 10.1109/jiot.2022.3155697 |
| Fields | Computer Science,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4214897012 |
| Type | article |
| Year | 2022 |

## Paper authors

- [Yang Li](https://scholariq.org/researchers/yang-li/)

## Paper primary topic

- [Underwater Vehicles and Communication Systems](https://scholariq.org/topics/underwater-vehicles-and-communication-systems/)

## Paper topics

- [Underwater Vehicles and Communication Systems](https://scholariq.org/topics/underwater-vehicles-and-communication-systems/)
- [Distributed Control Multi-Agent Systems](https://scholariq.org/topics/distributed-control-multi-agent-systems/)
- [Reinforcement Learning in Robotics](https://scholariq.org/topics/reinforcement-learning-in-robotics/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.