Summary of Semantic and Effective Communication For Remote Control Tasks with Dynamic Feature Compression, by Pietro Talli et al.
Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression
by Pietro Talli, Francesco Pase, Federico Chiariotti, Andrea Zanella, Michele Zorzi
First submitted to arxiv on: 14 Jan 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 The paper presents a solution to optimize communication between an observer and an actor in a remote wireless control system. The goal is to reduce irrelevant sensory information transmitted wirelessly, a crucial issue in 5G and beyond systems. The authors model the problem as a Partially Observable Markov Decision Process (POMDP) and develop a novel approach using Vector Quantized Variational Autoencoder (VQ-VAE) encoding and Deep Reinforcement Learning (DRL). The proposed method dynamically adjusts the quantization level based on the current state of the environment and past messages. The authors test their approach on the CartPole reference control problem, achieving significant performance improvements over traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make communication between robots or machines faster and more efficient. When lots of information needs to be shared wirelessly, it can get slowed down by unnecessary data. To solve this problem, the authors created a new way to compress and send information using artificial intelligence (AI) techniques. They used a special kind of AI called Deep Reinforcement Learning (DRL) to adjust how much information is sent based on what’s happening in real-time. The authors tested their approach on a well-known challenge and saw significant improvements. |
Keywords
* Artificial intelligence * Quantization * Reinforcement learning * Variational autoencoder
