# Task-Oriented Communication Design at Scale

arXiv: https://arxiv.org/abs/2305.08481 · HTML: https://mlsanity.com/paper/2305.08481/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 54 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 28 of 28)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Fixed Point Quantization of Deep Convolutional Networks](https://mlsanity.com/paper/1511.06393/) (2015)
- [On the Pitfalls of Measuring Emergent Communication](https://mlsanity.com/paper/1903.05168/) (2019)
- [Task-Oriented Communication for Multi-Device Cooperative Edge Inference](https://mlsanity.com/paper/2109.00172/) (2021)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [Deep Task-Based Quantization](https://mlsanity.com/paper/1908.06845/) (2019)
- [The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.07144/) (2021)
- [Goal-Oriented Communications for the IoT and Application to Data Compression](https://mlsanity.com/paper/2211.05378/) (2022)
- [Rate-cost tradeoffs in control](https://mlsanity.com/paper/1612.02126/) (2016)
- [An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation](https://mlsanity.com/paper/2201.12477/) (2022)
- [Value of Information in Feedback Control: Quantification](https://mlsanity.com/paper/1812.07534/) (2018)
- [Task-oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications](https://mlsanity.com/paper/2102.07166/) (2021)
- [Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels](https://mlsanity.com/paper/2005.14220/) (2020)
- [COME TOGETHER: Multi-Agent Geometric Consensus (Gathering, Rendezvous, Clustering, Aggregation)](https://mlsanity.com/paper/1902.01455/) (2019)
- [Task-Based Quantization with Application to MIMO Receivers](https://mlsanity.com/paper/2002.04290/) (2020)
- [Learning to Communicate with Intent: An Introduction](https://mlsanity.com/paper/2211.09613/) (2022)
- [Task-Effective Compression of Observations for the Centralized Control of a Multi-agent System Over Bit-Budgeted Channels](https://mlsanity.com/paper/2301.01628/) (2023)

## Built on by (most cited first, 3 of 3)

- [Diff-GOn: Enhancing Diffusion Models for Goal-Oriented Communications](https://mlsanity.com/paper/2412.06980/) (2024)
- [Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication](https://mlsanity.com/paper/2412.10265/) (2024)
- [Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory](https://mlsanity.com/paper/2603.23967/) (2026)

## Abstract (arXiv)

With countless promising applications in various domains such as IoT and industry 4.0, task-oriented communication design (TOCD) is getting accelerated attention from the research community. This paper presents a novel approach for designing scalable task-oriented quantization and communications in cooperative multi-agent systems (MAS). The proposed approach utilizes the TOCD framework and the value of information (VoI) concept to enable efficient communication of quantized observations among agents while maximizing the average return performance of the MAS, a parameter that quantifies the MAS's task effectiveness. The computational complexity of learning the VoI, however, grows exponentially with the number of agents. Thus, we propose a three-step framework: i) learning the VoI (using reinforcement learning (RL)) for a two-agent system, ii) designing the quantization policy for an N-agent MAS using the learned VoI for a range of bit-budgets and, (iii) learning the agents' control policies using RL while following the designed quantization policies in the earlier step. We observe that one can reduce the computational cost of obtaining the value of information by exploiting insights gained from studying a similar two-agent system - instead of the original N-agent system. We then quantize agents' observations such that their more valuable observations are communicated more precisely. Our analytical results show the applicability of the proposed framework under a wide range of problems. Numerical results show striking improvements in reducing the computational complexity of obtaining VoI needed for the TOCD in a MAS problem without compromising the average return performance of the MAS.