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| Content Provider | frontiers |
|---|---|
| Author | Havenstrøm, Simen Theie Rasheed, Adil San, Omer |
| Abstract | Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights about the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path-following and collision avoidance, decision making becomes non-trivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques, to develop autonomous agents capable of achieving this hybrid objective without having à priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path-following and avoiding collisions toward achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations. |
| ISSN | 22969144 |
| DOI | 10.3389/frobt.2020.566037 |
| Volume Number | 7 |
| Journal | Frontiers in Robotics and AI |
| Language | English |
| Publisher Date | 2021-01-25 |
| Access Restriction | Open |
| Subject Keyword | Path following Continuous control Collision avoidance Autonomous under water vehicle Deep reinforcement learning (DRL) Curriculum learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Science Applications |
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