Summary of Variational Autoencoders For Exteroceptive Perception in Reinforcement Learning-based Collision Avoidance, by Thomas Nakken Larsen et al.
Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
by Thomas Nakken Larsen, Eirik Runde Barlaug, Adil Rasheed
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore the application of machine learning algorithms, specifically Deep Reinforcement Learning (DRL), to improve the performance and adaptability of marine transportation systems. The authors highlight the potential for DRL to integrate path-following and collision avoidance with multiple obstacles. However, current DRL algorithms require significant computational resources when dealing with large searchable parameter spaces. To address this challenge, the researchers propose the use of Variational AutoEncoders (VAEs) to compress high-fidelity sensor data into a low-dimensional latent encoding. This encoding serves as input to a DRL agent that is trained and evaluated in a stochastic simulation environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Marine transportation systems are getting smarter with machine learning algorithms like Deep Reinforcement Learning (DRL). Right now, this technology can help boats navigate safely around obstacles. But it requires a lot of computer power, which can be a problem when dealing with big data. To make things more efficient, the researchers came up with an idea to use something called Variational AutoEncoders (VAEs) to shrink down important sensor information into a smaller package that DRL can work with. They tested this approach in a simulated environment and found it works well for maritime control systems. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
